diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 9a30054bfef..ac301183214 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -1,6 +1,6 @@ name: build -on: [push, pull_request] +on: [ push, pull_request ] jobs: lint: @@ -29,25 +29,26 @@ jobs: UBUNTU_VERSION: ubuntu1804 FORCE_CUDA: 1 MMCV_CUDA_ARGS: -gencode=arch=compute_61,code=sm_61 - runs-on: ubuntu-latest + runs-on: ubuntu-18.04 strategy: matrix: - python-version: [3.6, 3.7] - torch: [1.3.0+cpu, 1.5.0+cpu] + python-version: [ 3.6, 3.7 ] + torch: [ 1.3.0+cpu, 1.5.0+cpu, 1.5.0+cu101, 1.6.0+cu101, 1.7.0+cu101, 1.8.0+cu101 ] include: - torch: 1.3.0+cpu torchvision: 0.4.1+cpu - torch: 1.5.0+cpu torchvision: 0.6.0+cpu - - torch: 1.5.0+cpu - torchvision: 0.6.0+cpu - python-version: 3.8 - torch: 1.5.0+cu101 torchvision: 0.6.0+cu101 - python-version: 3.7 - torch: 1.6.0+cu101 torchvision: 0.7.0+cu101 - python-version: 3.7 + - torch: 1.7.0+cu101 + torchvision: 0.8.1+cu101 + - torch: 1.8.0+cu101 + torchvision: 0.9.0+cu101 + - torch: 1.8.0+cu101 + torchvision: 0.9.0+cu101 steps: - uses: actions/checkout@v2 @@ -56,7 +57,7 @@ jobs: with: python-version: ${{ matrix.python-version }} - name: Install CUDA - if: ${{matrix.torch == '1.5.0+cu101'}} + if: ${{matrix.torch == '1.5.0+cu101' || matrix.torch == '1.6.0+cu101' || matrix.torch == '1.7.0+cu101' || matrix.torch == '1.8.0+cu101'}} run: | export INSTALLER=cuda-repo-${UBUNTU_VERSION}_${CUDA}_amd64.deb wget http://developer.download.nvidia.com/compute/cuda/repos/${UBUNTU_VERSION}/x86_64/${INSTALLER} @@ -88,7 +89,7 @@ jobs: coverage report -m # Only upload coverage report for python3.7 && pytorch1.5 - name: Upload coverage to Codecov - if: ${{matrix.torch == '1.5.0+cu101' && matrix.python-version == '3.7'}} + if: ${{matrix.torch == '1.8.0+cu101' && matrix.python-version == '3.7'}} uses: codecov/codecov-action@v1.0.10 with: file: ./coverage.xml diff --git a/configs/_base_/schedules/schedule_160k_epochwise.py b/configs/_base_/schedules/schedule_160k_epochwise.py deleted file mode 100644 index 1da307e680e..00000000000 --- a/configs/_base_/schedules/schedule_160k_epochwise.py +++ /dev/null @@ -1,9 +0,0 @@ -# optimizer -optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) -optimizer_config = dict() -# learning policy -lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) -# runtime settings -runner = dict(type='IterBasedRunner', max_iters=160000) -checkpoint_config = dict(by_epoch=True, interval=16000) -evaluation = dict(interval=16000, metric='mIoU') diff --git a/configs/_base_/schedules/schedule_40k_epochwise.py b/configs/_base_/schedules/schedule_40k_epochwise.py deleted file mode 100644 index 549d20161c7..00000000000 --- a/configs/_base_/schedules/schedule_40k_epochwise.py +++ /dev/null @@ -1,9 +0,0 @@ -# optimizer -optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) -optimizer_config = dict() -# learning policy -lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=True) -# runtime settings -runner = dict(type='IterBasedRunner', max_iters=40000) -checkpoint_config = dict(by_epoch=True, interval=4000) -evaluation = dict(interval=4000, metric='mIoU') diff --git a/configs/_base_/schedules/schedule_80k_epochwisse.py b/configs/_base_/schedules/schedule_80k_epochwisse.py deleted file mode 100644 index 371dba1e7d2..00000000000 --- a/configs/_base_/schedules/schedule_80k_epochwisse.py +++ /dev/null @@ -1,9 +0,0 @@ -# optimizer -optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) -optimizer_config = dict() -# learning policy -lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) -# runtime settings -runner = dict(type='IterBasedRunner', max_iters=80000) -checkpoint_config = dict(by_epoch=True, interval=8000) -evaluation = dict(interval=8000, metric='mIoU') diff --git a/configs/ann/README.md b/configs/ann/README.md index 7fc1648311d..3bc332aa852 100644 --- a/configs/ann/README.md +++ b/configs/ann/README.md @@ -22,31 +22,31 @@ ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| ANN | R-50-D8 | 512x1024 | 40000 | 6 | 3.71 | 77.40 | 78.57 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211-049fc292.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211.log.json) | -| ANN | R-101-D8 | 512x1024 | 40000 | 9.5 | 2.55 | 76.55 | 78.85 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243-adf6eece.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243.log.json) | -| ANN | R-50-D8 | 769x769 | 40000 | 6.8 | 1.70 | 78.89 | 80.46 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712-2b46b04d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712.log.json) | -| ANN | R-101-D8 | 769x769 | 40000 | 10.7 | 1.15 | 79.32 | 80.94 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720-059bff28.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720.log.json) | -| ANN | R-50-D8 | 512x1024 | 80000 | - | - | 77.34 | 78.65 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911-5a9ad545.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911.log.json) | -| ANN | R-101-D8 | 512x1024 | 80000 | - | - | 77.14 | 78.81 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728-aceccc6e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728.log.json) | -| ANN | R-50-D8 | 769x769 | 80000 | - | - | 78.88 | 80.57 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426-cc7ff323.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426.log.json) | -| ANN | R-101-D8 | 769x769 | 80000 | - | - | 78.80 | 80.34 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713-a9d4be8d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| ANN | R-50-D8 | 512x1024 | 40000 | 6 | 3.71 | 77.40 | 78.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211-049fc292.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_40k_cityscapes/ann_r50-d8_512x1024_40k_cityscapes_20200605_095211.log.json) | +| ANN | R-101-D8 | 512x1024 | 40000 | 9.5 | 2.55 | 76.55 | 78.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243-adf6eece.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_40k_cityscapes/ann_r101-d8_512x1024_40k_cityscapes_20200605_095243.log.json) | +| ANN | R-50-D8 | 769x769 | 40000 | 6.8 | 1.70 | 78.89 | 80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712-2b46b04d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_40k_cityscapes/ann_r50-d8_769x769_40k_cityscapes_20200530_025712.log.json) | +| ANN | R-101-D8 | 769x769 | 40000 | 10.7 | 1.15 | 79.32 | 80.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720-059bff28.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_40k_cityscapes/ann_r101-d8_769x769_40k_cityscapes_20200530_025720.log.json) | +| ANN | R-50-D8 | 512x1024 | 80000 | - | - | 77.34 | 78.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911-5a9ad545.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x1024_80k_cityscapes/ann_r50-d8_512x1024_80k_cityscapes_20200607_101911.log.json) | +| ANN | R-101-D8 | 512x1024 | 80000 | - | - | 77.14 | 78.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728-aceccc6e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x1024_80k_cityscapes/ann_r101-d8_512x1024_80k_cityscapes_20200607_013728.log.json) | +| ANN | R-50-D8 | 769x769 | 80000 | - | - | 78.88 | 80.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426-cc7ff323.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_769x769_80k_cityscapes/ann_r50-d8_769x769_80k_cityscapes_20200607_044426.log.json) | +| ANN | R-101-D8 | 769x769 | 80000 | - | - | 78.80 | 80.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713-a9d4be8d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_769x769_80k_cityscapes/ann_r101-d8_769x769_80k_cityscapes_20200607_013713.log.json) | ### ADE20K -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| ANN | R-50-D8 | 512x512 | 80000 | 9.1 | 21.01 | 41.01 | 42.30 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818-26f75e11.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818.log.json) | -| ANN | R-101-D8 | 512x512 | 80000 | 12.5 | 14.12 | 42.94 | 44.18 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818-c0153543.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818.log.json) | -| ANN | R-50-D8 | 512x512 | 160000 | - | - | 41.74 | 42.62 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733-892247bc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733.log.json) | -| ANN | R-101-D8 | 512x512 | 160000 | - | - | 42.94 | 44.06 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733-955eb1ec.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| ANN | R-50-D8 | 512x512 | 80000 | 9.1 | 21.01 | 41.01 | 42.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818-26f75e11.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_80k_ade20k/ann_r50-d8_512x512_80k_ade20k_20200615_014818.log.json) | +| ANN | R-101-D8 | 512x512 | 80000 | 12.5 | 14.12 | 42.94 | 44.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818-c0153543.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_80k_ade20k/ann_r101-d8_512x512_80k_ade20k_20200615_014818.log.json) | +| ANN | R-50-D8 | 512x512 | 160000 | - | - | 41.74 | 42.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733-892247bc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_160k_ade20k/ann_r50-d8_512x512_160k_ade20k_20200615_231733.log.json) | +| ANN | R-101-D8 | 512x512 | 160000 | - | - | 42.94 | 44.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733-955eb1ec.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_160k_ade20k/ann_r101-d8_512x512_160k_ade20k_20200615_231733.log.json) | ### Pascal VOC 2012 + Aug -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| ANN | R-50-D8 | 512x512 | 20000 | 6 | 20.92 | 74.86 | 76.13 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246-dfcb1c62.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246.log.json) | -| ANN | R-101-D8 | 512x512 | 20000 | 9.5 | 13.94 | 77.47 | 78.70 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246-2fad0042.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246.log.json) | -| ANN | R-50-D8 | 512x512 | 40000 | - | - | 76.56 | 77.51 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314-b5dac322.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314.log.json) | -| ANN | R-101-D8 | 512x512 | 40000 | - | - | 76.70 | 78.06 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314-bd205bbe.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| ANN | R-50-D8 | 512x512 | 20000 | 6 | 20.92 | 74.86 | 76.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246-dfcb1c62.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_20k_voc12aug/ann_r50-d8_512x512_20k_voc12aug_20200617_222246.log.json) | +| ANN | R-101-D8 | 512x512 | 20000 | 9.5 | 13.94 | 77.47 | 78.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246-2fad0042.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_20k_voc12aug/ann_r101-d8_512x512_20k_voc12aug_20200617_222246.log.json) | +| ANN | R-50-D8 | 512x512 | 40000 | - | - | 76.56 | 77.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314-b5dac322.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r50-d8_512x512_40k_voc12aug/ann_r50-d8_512x512_40k_voc12aug_20200613_231314.log.json) | +| ANN | R-101-D8 | 512x512 | 40000 | - | - | 76.70 | 78.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann/ann_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314-bd205bbe.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ann/ann_r101-d8_512x512_40k_voc12aug/ann_r101-d8_512x512_40k_voc12aug_20200613_231314.log.json) | diff --git a/configs/apcnet/README.md b/configs/apcnet/README.md index c2ab106a29c..9366cb37284 100644 --- a/configs/apcnet/README.md +++ b/configs/apcnet/README.md @@ -18,22 +18,22 @@ year = {2019} ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| APCNet | R-50-D8 | 512x1024 | 40000 | 7.7 | 3.57 | 78.02 | 79.26 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes-20201214_115717.log.json) | -| APCNet | R-101-D8 | 512x1024 | 40000 | 11.2 | 2.15 | 79.08 | 80.34 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes-20201214_115716.log.json) | -| APCNet | R-50-D8 | 769x769 | 40000 | 8.7 | 1.52 | 77.89 | 79.75 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes-20201214_115717.log.json) | -| APCNet | R-101-D8 | 769x769 | 40000 | 12.7 | 1.03 | 77.96 | 79.24 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes-20201214_115718.log.json) | -| APCNet | R-50-D8 | 512x1024 | 80000 | - | - | 78.96 | 79.94 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes-20201214_115716.log.json) | -| APCNet | R-101-D8 | 512x1024 | 80000 | - | - | 79.64 | 80.61 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes-20201214_115705.log.json) | -| APCNet | R-50-D8 | 769x769 | 80000 | - | - | 78.79 | 80.35 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes-20201214_115718.log.json) | -| APCNet | R-101-D8 | 769x769 | 80000 | - | - | 78.45 | 79.91 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes-20201214_115716.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| APCNet | R-50-D8 | 512x1024 | 40000 | 7.7 | 3.57 | 78.02 | 79.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_40k_cityscapes/apcnet_r50-d8_512x1024_40k_cityscapes-20201214_115717.log.json) | +| APCNet | R-101-D8 | 512x1024 | 40000 | 11.2 | 2.15 | 79.08 | 80.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_40k_cityscapes/apcnet_r101-d8_512x1024_40k_cityscapes-20201214_115716.log.json) | +| APCNet | R-50-D8 | 769x769 | 40000 | 8.7 | 1.52 | 77.89 | 79.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_40k_cityscapes/apcnet_r50-d8_769x769_40k_cityscapes-20201214_115717.log.json) | +| APCNet | R-101-D8 | 769x769 | 40000 | 12.7 | 1.03 | 77.96 | 79.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_40k_cityscapes/apcnet_r101-d8_769x769_40k_cityscapes-20201214_115718.log.json) | +| APCNet | R-50-D8 | 512x1024 | 80000 | - | - | 78.96 | 79.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x1024_80k_cityscapes/apcnet_r50-d8_512x1024_80k_cityscapes-20201214_115716.log.json) | +| APCNet | R-101-D8 | 512x1024 | 80000 | - | - | 79.64 | 80.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x1024_80k_cityscapes/apcnet_r101-d8_512x1024_80k_cityscapes-20201214_115705.log.json) | +| APCNet | R-50-D8 | 769x769 | 80000 | - | - | 78.79 | 80.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_769x769_80k_cityscapes/apcnet_r50-d8_769x769_80k_cityscapes-20201214_115718.log.json) | +| APCNet | R-101-D8 | 769x769 | 80000 | - | - | 78.45 | 79.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_769x769_80k_cityscapes/apcnet_r101-d8_769x769_80k_cityscapes-20201214_115716.log.json) | ### ADE20K -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| APCNet | R-50-D8 | 512x512 | 80000 | 10.1 | 19.61 | 42.20 | 43.30 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k-20201214_115705.log.json) | -| APCNet | R-101-D8 | 512x512 | 80000 | 13.6 | 13.10 | 45.54 | 46.65 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k-20201214_115704.log.json) | -| APCNet | R-50-D8 | 512x512 | 160000 | - | - | 43.40 | 43.94 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k-20201214_115706.log.json) | -| APCNet | R-101-D8 | 512x512 | 160000 | - | - | 45.41 | 46.63 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k-20201214_115705.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| APCNet | R-50-D8 | 512x512 | 80000 | 10.1 | 19.61 | 42.20 | 43.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_80k_ade20k/apcnet_r50-d8_512x512_80k_ade20k-20201214_115705.log.json) | +| APCNet | R-101-D8 | 512x512 | 80000 | 13.6 | 13.10 | 45.54 | 46.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_80k_ade20k/apcnet_r101-d8_512x512_80k_ade20k-20201214_115704.log.json) | +| APCNet | R-50-D8 | 512x512 | 160000 | - | - | 43.40 | 43.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r50-d8_512x512_160k_ade20k/apcnet_r50-d8_512x512_160k_ade20k-20201214_115706.log.json) | +| APCNet | R-101-D8 | 512x512 | 160000 | - | - | 45.41 | 46.63 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet/apcnet_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/apcnet/apcnet_r101-d8_512x512_160k_ade20k/apcnet_r101-d8_512x512_160k_ade20k-20201214_115705.log.json) | diff --git a/configs/ccnet/README.md b/configs/ccnet/README.md index 044d5896781..9885239e99a 100644 --- a/configs/ccnet/README.md +++ b/configs/ccnet/README.md @@ -17,31 +17,31 @@ ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| CCNet | R-50-D8 | 512x1024 | 40000 | 6 | 3.32 | 77.76 | 78.87 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517-4123f401.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517.log.json) | -| CCNet | R-101-D8 | 512x1024 | 40000 | 9.5 | 2.31 | 76.35 | 78.19 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540-a3b84ba6.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540.log.json) | -| CCNet | R-50-D8 | 769x769 | 40000 | 6.8 | 1.43 | 78.46 | 79.93 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125-76d11884.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125.log.json) | -| CCNet | R-101-D8 | 769x769 | 40000 | 10.7 | 1.01 | 76.94 | 78.62 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428-4f57c8d0.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428.log.json) | -| CCNet | R-50-D8 | 512x1024 | 80000 | - | - | 79.03 | 80.16 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421-869a3423.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421.log.json) | -| CCNet | R-101-D8 | 512x1024 | 80000 | - | - | 78.87 | 79.90 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935-ffae8917.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935.log.json) | -| CCNet | R-50-D8 | 769x769 | 80000 | - | - | 79.29 | 81.08 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421-73eed8ca.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421.log.json) | -| CCNet | R-101-D8 | 769x769 | 80000 | - | - | 79.45 | 80.66 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502-ad3cd481.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| CCNet | R-50-D8 | 512x1024 | 40000 | 6 | 3.32 | 77.76 | 78.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517-4123f401.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_40k_cityscapes/ccnet_r50-d8_512x1024_40k_cityscapes_20200616_142517.log.json) | +| CCNet | R-101-D8 | 512x1024 | 40000 | 9.5 | 2.31 | 76.35 | 78.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540-a3b84ba6.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_40k_cityscapes/ccnet_r101-d8_512x1024_40k_cityscapes_20200616_142540.log.json) | +| CCNet | R-50-D8 | 769x769 | 40000 | 6.8 | 1.43 | 78.46 | 79.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125-76d11884.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_40k_cityscapes/ccnet_r50-d8_769x769_40k_cityscapes_20200616_145125.log.json) | +| CCNet | R-101-D8 | 769x769 | 40000 | 10.7 | 1.01 | 76.94 | 78.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428-4f57c8d0.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_40k_cityscapes/ccnet_r101-d8_769x769_40k_cityscapes_20200617_101428.log.json) | +| CCNet | R-50-D8 | 512x1024 | 80000 | - | - | 79.03 | 80.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421-869a3423.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x1024_80k_cityscapes/ccnet_r50-d8_512x1024_80k_cityscapes_20200617_010421.log.json) | +| CCNet | R-101-D8 | 512x1024 | 80000 | - | - | 78.87 | 79.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935-ffae8917.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x1024_80k_cityscapes/ccnet_r101-d8_512x1024_80k_cityscapes_20200617_203935.log.json) | +| CCNet | R-50-D8 | 769x769 | 80000 | - | - | 79.29 | 81.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421-73eed8ca.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_769x769_80k_cityscapes/ccnet_r50-d8_769x769_80k_cityscapes_20200617_010421.log.json) | +| CCNet | R-101-D8 | 769x769 | 80000 | - | - | 79.45 | 80.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502-ad3cd481.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_769x769_80k_cityscapes/ccnet_r101-d8_769x769_80k_cityscapes_20200618_011502.log.json) | ### ADE20K -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| CCNet | R-50-D8 | 512x512 | 80000 | 8.8 | 20.89 | 41.78 | 42.98 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848-aa37f61e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848.log.json) | -| CCNet | R-101-D8 | 512x512 | 80000 | 12.2 | 14.11 | 43.97 | 45.13 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848-1f4929a3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848.log.json) | -| CCNet | R-50-D8 | 512x512 | 160000 | - | - | 42.08 | 43.13 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435-7c97193b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435.log.json) | -| CCNet | R-101-D8 | 512x512 | 160000 | - | - | 43.71 | 45.04 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644-e849e007.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| CCNet | R-50-D8 | 512x512 | 80000 | 8.8 | 20.89 | 41.78 | 42.98 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848-aa37f61e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_80k_ade20k/ccnet_r50-d8_512x512_80k_ade20k_20200615_014848.log.json) | +| CCNet | R-101-D8 | 512x512 | 80000 | 12.2 | 14.11 | 43.97 | 45.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848-1f4929a3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_80k_ade20k/ccnet_r101-d8_512x512_80k_ade20k_20200615_014848.log.json) | +| CCNet | R-50-D8 | 512x512 | 160000 | - | - | 42.08 | 43.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435-7c97193b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_160k_ade20k/ccnet_r50-d8_512x512_160k_ade20k_20200616_084435.log.json) | +| CCNet | R-101-D8 | 512x512 | 160000 | - | - | 43.71 | 45.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644-e849e007.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_160k_ade20k/ccnet_r101-d8_512x512_160k_ade20k_20200616_000644.log.json) | ### Pascal VOC 2012 + Aug -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| CCNet | R-50-D8 | 512x512 | 20000 | 6 | 20.45 | 76.17 | 77.51 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212-fad81784.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212.log.json) | -| CCNet | R-101-D8 | 512x512 | 20000 | 9.5 | 13.64 | 77.27 | 79.02 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212-0007b61d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212.log.json) | -| CCNet | R-50-D8 | 512x512 | 40000 | - | - | 75.96 | 77.04 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127-c2a15f02.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127.log.json) | -| CCNet | R-101-D8 | 512x512 | 40000 | - | - | 77.87 | 78.90 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127-c30da577.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| CCNet | R-50-D8 | 512x512 | 20000 | 6 | 20.45 | 76.17 | 77.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212-fad81784.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_20k_voc12aug/ccnet_r50-d8_512x512_20k_voc12aug_20200617_193212.log.json) | +| CCNet | R-101-D8 | 512x512 | 20000 | 9.5 | 13.64 | 77.27 | 79.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212-0007b61d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_20k_voc12aug/ccnet_r101-d8_512x512_20k_voc12aug_20200617_193212.log.json) | +| CCNet | R-50-D8 | 512x512 | 40000 | - | - | 75.96 | 77.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127-c2a15f02.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r50-d8_512x512_40k_voc12aug/ccnet_r50-d8_512x512_40k_voc12aug_20200613_232127.log.json) | +| CCNet | R-101-D8 | 512x512 | 40000 | - | - | 77.87 | 78.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet/ccnet_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127-c30da577.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ccnet/ccnet_r101-d8_512x512_40k_voc12aug/ccnet_r101-d8_512x512_40k_voc12aug_20200613_232127.log.json) | diff --git a/configs/cgnet/README.md b/configs/cgnet/README.md index 00ba387203a..4859492ebf1 100644 --- a/configs/cgnet/README.md +++ b/configs/cgnet/README.md @@ -17,7 +17,7 @@ ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|-----------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| CGNet | M3N21 | 680x680 | 60000 | 7.5 | 30.51 | 65.63 | 68.04 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes_20201101_110253-4c0b2f2d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes-20201101_110253.log.json) | -| CGNet | M3N21 | 512x1024 | 60000 | 8.3 | 31.14 | 68.27 | 70.33 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes_20201101_110254-124ea03b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes-20201101_110254.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| CGNet | M3N21 | 680x680 | 60000 | 7.5 | 30.51 | 65.63 | 68.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/cgnet/cgnet_680x680_60k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes_20201101_110253-4c0b2f2d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_680x680_60k_cityscapes/cgnet_680x680_60k_cityscapes-20201101_110253.log.json) | +| CGNet | M3N21 | 512x1024 | 60000 | 8.3 | 31.14 | 68.27 | 70.33 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/cgnet/cgnet_512x1024_60k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes_20201101_110254-124ea03b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/cgnet/cgnet_512x1024_60k_cityscapes/cgnet_512x1024_60k_cityscapes-20201101_110254.log.json) | diff --git a/configs/danet/README.md b/configs/danet/README.md index f49ccf96194..90ddf6cab88 100644 --- a/configs/danet/README.md +++ b/configs/danet/README.md @@ -17,31 +17,31 @@ ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|---------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| DANet | R-50-D8 | 512x1024 | 40000 | 7.4 | 2.66 | 78.74 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324.log.json) | -| DANet | R-101-D8 | 512x1024 | 40000 | 10.9 | 1.99 | 80.52 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831.log.json) | -| DANet | R-50-D8 | 769x769 | 40000 | 8.8 | 1.56 | 78.88 | 80.62 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703.log.json) | -| DANet | R-101-D8 | 769x769 | 40000 | 12.8 | 1.07 | 79.88 | 81.47 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717.log.json) | -| DANet | R-50-D8 | 512x1024 | 80000 | - | - | 79.34 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029.log.json) | -| DANet | R-101-D8 | 512x1024 | 80000 | - | - | 80.41 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918.log.json) | -| DANet | R-50-D8 | 769x769 | 80000 | - | - | 79.27 | 80.96 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954.log.json) | -| DANet | R-101-D8 | 769x769 | 80000 | - | - | 80.47 | 82.02 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| DANet | R-50-D8 | 512x1024 | 40000 | 7.4 | 2.66 | 78.74 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324.log.json) | +| DANet | R-101-D8 | 512x1024 | 40000 | 10.9 | 1.99 | 80.52 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831.log.json) | +| DANet | R-50-D8 | 769x769 | 40000 | 8.8 | 1.56 | 78.88 | 80.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703.log.json) | +| DANet | R-101-D8 | 769x769 | 40000 | 12.8 | 1.07 | 79.88 | 81.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717.log.json) | +| DANet | R-50-D8 | 512x1024 | 80000 | - | - | 79.34 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029.log.json) | +| DANet | R-101-D8 | 512x1024 | 80000 | - | - | 80.41 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918.log.json) | +| DANet | R-50-D8 | 769x769 | 80000 | - | - | 79.27 | 80.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954.log.json) | +| DANet | R-101-D8 | 769x769 | 80000 | - | - | 80.47 | 82.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918.log.json) | ### ADE20K -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| DANet | R-50-D8 | 512x512 | 80000 | 11.5 | 21.20 | 41.66 | 42.90 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125.log.json) | -| DANet | R-101-D8 | 512x512 | 80000 | 15 | 14.18 | 43.64 | 45.19 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126.log.json) | -| DANet | R-50-D8 | 512x512 | 160000 | - | - | 42.45 | 43.25 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340.log.json) | -| DANet | R-101-D8 | 512x512 | 160000 | - | - | 44.17 | 45.02 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| DANet | R-50-D8 | 512x512 | 80000 | 11.5 | 21.20 | 41.66 | 42.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125.log.json) | +| DANet | R-101-D8 | 512x512 | 80000 | 15 | 14.18 | 43.64 | 45.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126.log.json) | +| DANet | R-50-D8 | 512x512 | 160000 | - | - | 42.45 | 43.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340.log.json) | +| DANet | R-101-D8 | 512x512 | 160000 | - | - | 44.17 | 45.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348.log.json) | ### Pascal VOC 2012 + Aug -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| DANet | R-50-D8 | 512x512 | 20000 | 6.5 | 20.94 | 74.45 | 75.69 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026.log.json) | -| DANet | R-101-D8 | 512x512 | 20000 | 9.9 | 13.76 | 76.02 | 77.23 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026.log.json) | -| DANet | R-50-D8 | 512x512 | 40000 | - | - | 76.37 | 77.29 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526.log.json) | -| DANet | R-101-D8 | 512x512 | 40000 | - | - | 76.51 | 77.32 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| DANet | R-50-D8 | 512x512 | 20000 | 6.5 | 20.94 | 74.45 | 75.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026.log.json) | +| DANet | R-101-D8 | 512x512 | 20000 | 9.9 | 13.76 | 76.02 | 77.23 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026.log.json) | +| DANet | R-50-D8 | 512x512 | 40000 | - | - | 76.37 | 77.29 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526.log.json) | +| DANet | R-101-D8 | 512x512 | 40000 | - | - | 76.51 | 77.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031.log.json) | diff --git a/configs/deeplabv3/README.md b/configs/deeplabv3/README.md index d636c189488..970a779c7d1 100644 --- a/configs/deeplabv3/README.md +++ b/configs/deeplabv3/README.md @@ -19,51 +19,51 @@ Note: `D-8` here corresponding to the output stride 8 setting for DeepLab series ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|-----------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| DeepLabV3 | R-50-D8 | 512x1024 | 40000 | 6.1 | 2.57 | 79.09 | 80.45 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449.log.json) | -| DeepLabV3 | R-101-D8 | 512x1024 | 40000 | 9.6 | 1.92 | 77.12 | 79.61 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241.log.json) | -| DeepLabV3 | R-50-D8 | 769x769 | 40000 | 6.9 | 1.11 | 78.58 | 79.89 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723.log.json) | -| DeepLabV3 | R-101-D8 | 769x769 | 40000 | 10.9 | 0.83 | 79.27 | 80.11 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809.log.json) | -| DeepLabV3 | R-18-D8 | 512x1024 | 80000 | 1.7 | 13.78 | 76.70 | 78.27 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes-20201225_021506.log.json) | -| DeepLabV3 | R-50-D8 | 512x1024 | 80000 | - | - | 79.32 | 80.57 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404.log.json) | -| DeepLabV3 | R-101-D8 | 512x1024 | 80000 | - | - | 80.20 | 81.21 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503.log.json) | -| DeepLabV3 | R-18-D8 | 769x769 | 80000 | 1.9 | 5.55 | 76.60 | 78.26 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes-20201225_021506.log.json) | -| DeepLabV3 | R-50-D8 | 769x769 | 80000 | - | - | 79.89 | 81.06 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338.log.json) | -| DeepLabV3 | R-101-D8 | 769x769 | 80000 | - | - | 79.67 | 80.81 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353.log.json) | -| DeepLabV3 | R-101-D16-MG124 | 512x1024 | 40000 | 4.7 | - 6.96 | 76.71 | 78.63 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-67b0c992.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes-20200908_005644.log.json) | -| DeepLabV3 | R-101-D16-MG124 | 512x1024 | 80000 | - | - | 78.36 | 79.84 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes-20200908_005644.log.json) | -| DeepLabV3 | R-18b-D8 | 512x1024 | 80000 | 1.6 | 13.93 | 76.26 | 77.88 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes-20201225_094144.log.json) | -| DeepLabV3 | R-50b-D8 | 512x1024 | 80000 | 6.0 | 2.74 | 79.63 | 80.98 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes-20201225_155148.log.json) | -| DeepLabV3 | R-101b-D8| 512x1024 | 80000 | 9.5 | 1.81 | 80.01 | 81.21 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes-20201226_171821.log.json) | -| DeepLabV3 | R-18b-D8 | 769x769 | 80000 | 1.8 | 5.79 | 76.63 | 77.51 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes-20201225_094144.log.json) | -| DeepLabV3 | R-50b-D8 | 769x769 | 80000 | 6.8 | 1.16 | 78.80 | 80.27 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes-20201225_155404.log.json) | -| DeepLabV3 | R-101b-D8| 769x769 | 80000 | 10.7 | 0.82 | 79.41 | 80.73 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes-20201226_190843.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| --------- | --------------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| DeepLabV3 | R-50-D8 | 512x1024 | 40000 | 6.1 | 2.57 | 79.09 | 80.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449-acadc2f8.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes/deeplabv3_r50-d8_512x1024_40k_cityscapes_20200605_022449.log.json) | +| DeepLabV3 | R-101-D8 | 512x1024 | 40000 | 9.6 | 1.92 | 77.12 | 79.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241-7fd3f799.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_40k_cityscapes/deeplabv3_r101-d8_512x1024_40k_cityscapes_20200605_012241.log.json) | +| DeepLabV3 | R-50-D8 | 769x769 | 40000 | 6.9 | 1.11 | 78.58 | 79.89 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723-7eda553c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_40k_cityscapes/deeplabv3_r50-d8_769x769_40k_cityscapes_20200606_113723.log.json) | +| DeepLabV3 | R-101-D8 | 769x769 | 40000 | 10.9 | 0.83 | 79.27 | 80.11 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809-c64f889f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_40k_cityscapes/deeplabv3_r101-d8_769x769_40k_cityscapes_20200606_113809.log.json) | +| DeepLabV3 | R-18-D8 | 512x1024 | 80000 | 1.7 | 13.78 | 76.70 | 78.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_512x1024_80k_cityscapes/deeplabv3_r18-d8_512x1024_80k_cityscapes-20201225_021506.log.json) | +| DeepLabV3 | R-50-D8 | 512x1024 | 80000 | - | - | 79.32 | 80.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404-b92cfdd4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x1024_80k_cityscapes/deeplabv3_r50-d8_512x1024_80k_cityscapes_20200606_113404.log.json) | +| DeepLabV3 | R-101-D8 | 512x1024 | 80000 | - | - | 80.20 | 81.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503-9e428899.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes/deeplabv3_r101-d8_512x1024_80k_cityscapes_20200606_113503.log.json) | +| DeepLabV3 | R-18-D8 | 769x769 | 80000 | 1.9 | 5.55 | 76.60 | 78.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes_20201225_021506-6452126a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18-d8_769x769_80k_cityscapes/deeplabv3_r18-d8_769x769_80k_cityscapes-20201225_021506.log.json) | +| DeepLabV3 | R-50-D8 | 769x769 | 80000 | - | - | 79.89 | 81.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338-788d6228.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_769x769_80k_cityscapes/deeplabv3_r50-d8_769x769_80k_cityscapes_20200606_221338.log.json) | +| DeepLabV3 | R-101-D8 | 769x769 | 80000 | - | - | 79.67 | 80.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353-60e95418.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_769x769_80k_cityscapes/deeplabv3_r101-d8_769x769_80k_cityscapes_20200607_013353.log.json) | +| DeepLabV3 | R-101-D16-MG124 | 512x1024 | 40000 | 4.7 | - 6.96 | 76.71 | 78.63 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-67b0c992.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_40k_cityscapes-20200908_005644.log.json) | +| DeepLabV3 | R-101-D16-MG124 | 512x1024 | 80000 | - | - | 78.36 | 79.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-57bb8425.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3_r101-d16-mg124_512x1024_80k_cityscapes-20200908_005644.log.json) | +| DeepLabV3 | R-18b-D8 | 512x1024 | 80000 | 1.6 | 13.93 | 76.26 | 77.88 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_512x1024_80k_cityscapes/deeplabv3_r18b-d8_512x1024_80k_cityscapes-20201225_094144.log.json) | +| DeepLabV3 | R-50b-D8 | 512x1024 | 80000 | 6.0 | 2.74 | 79.63 | 80.98 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes_20201225_155148-ec368954.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_512x1024_80k_cityscapes/deeplabv3_r50b-d8_512x1024_80k_cityscapes-20201225_155148.log.json) | +| DeepLabV3 | R-101b-D8 | 512x1024 | 80000 | 9.5 | 1.81 | 80.01 | 81.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes_20201226_171821-8fd49503.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_512x1024_80k_cityscapes/deeplabv3_r101b-d8_512x1024_80k_cityscapes-20201226_171821.log.json) | +| DeepLabV3 | R-18b-D8 | 769x769 | 80000 | 1.8 | 5.79 | 76.63 | 77.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes_20201225_094144-fdc985d9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r18b-d8_769x769_80k_cityscapes/deeplabv3_r18b-d8_769x769_80k_cityscapes-20201225_094144.log.json) | +| DeepLabV3 | R-50b-D8 | 769x769 | 80000 | 6.8 | 1.16 | 78.80 | 80.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes_20201225_155404-87fb0cf4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50b-d8_769x769_80k_cityscapes/deeplabv3_r50b-d8_769x769_80k_cityscapes-20201225_155404.log.json) | +| DeepLabV3 | R-101b-D8 | 769x769 | 80000 | 10.7 | 0.82 | 79.41 | 80.73 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes_20201226_190843-9142ee57.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101b-d8_769x769_80k_cityscapes/deeplabv3_r101b-d8_769x769_80k_cityscapes-20201226_190843.log.json) | ### ADE20K -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|-----------|----------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| DeepLabV3 | R-50-D8 | 512x512 | 80000 | 8.9 | 14.76 | 42.42 | 43.28 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028.log.json) | -| DeepLabV3 | R-101-D8 | 512x512 | 80000 | 12.4 | 10.14 | 44.08 | 45.19 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256.log.json) | -| DeepLabV3 | R-50-D8 | 512x512 | 160000 | - | - | 42.66 | 44.09 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227.log.json) | -| DeepLabV3 | R-101-D8 | 512x512 | 160000 | - | - | 45.00 | 46.66 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| DeepLabV3 | R-50-D8 | 512x512 | 80000 | 8.9 | 14.76 | 42.42 | 43.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028-0bb3f844.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_80k_ade20k/deeplabv3_r50-d8_512x512_80k_ade20k_20200614_185028.log.json) | +| DeepLabV3 | R-101-D8 | 512x512 | 80000 | 12.4 | 10.14 | 44.08 | 45.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256-d89c7fa4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_80k_ade20k/deeplabv3_r101-d8_512x512_80k_ade20k_20200615_021256.log.json) | +| DeepLabV3 | R-50-D8 | 512x512 | 160000 | - | - | 42.66 | 44.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227-5d0ee427.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_160k_ade20k/deeplabv3_r50-d8_512x512_160k_ade20k_20200615_123227.log.json) | +| DeepLabV3 | R-101-D8 | 512x512 | 160000 | - | - | 45.00 | 46.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816-b1f72b3b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k/deeplabv3_r101-d8_512x512_160k_ade20k_20200615_105816.log.json) | ### Pascal VOC 2012 + Aug -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|-----------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| DeepLabV3 | R-50-D8 | 512x512 | 20000 | 6.1 | 13.88 | 76.17 | 77.42 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906.log.json) | -| DeepLabV3 | R-101-D8 | 512x512 | 20000 | 9.6 | 9.81 | 78.70 | 79.95 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932.log.json) | -| DeepLabV3 | R-50-D8 | 512x512 | 40000 | - | - | 77.68 | 78.78 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546.log.json) | -| DeepLabV3 | R-101-D8 | 512x512 | 40000 | - | - | 77.92 | 79.18 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| DeepLabV3 | R-50-D8 | 512x512 | 20000 | 6.1 | 13.88 | 76.17 | 77.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906-596905ef.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_20k_voc12aug/deeplabv3_r50-d8_512x512_20k_voc12aug_20200617_010906.log.json) | +| DeepLabV3 | R-101-D8 | 512x512 | 20000 | 9.6 | 9.81 | 78.70 | 79.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932-8d13832f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_20k_voc12aug/deeplabv3_r101-d8_512x512_20k_voc12aug_20200617_010932.log.json) | +| DeepLabV3 | R-50-D8 | 512x512 | 40000 | - | - | 77.68 | 78.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546-2ae96e7e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r50-d8_512x512_40k_voc12aug/deeplabv3_r50-d8_512x512_40k_voc12aug_20200613_161546.log.json) | +| DeepLabV3 | R-101-D8 | 512x512 | 40000 | - | - | 77.92 | 79.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432-0017d784.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_512x512_40k_voc12aug/deeplabv3_r101-d8_512x512_40k_voc12aug_20200613_161432.log.json) | ### Pascal Context -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|-----------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| DeepLabV3 | R-101-D8 | 480x480 | 40000 | 9.2 | 7.09 | 46.55 | 47.81 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context-20200911_204118.log.json) | -| DeepLabV3 | R-101-D8 | 480x480 | 80000 | - | - | 46.42 | 47.53 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context-20200911_170155.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| --------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| DeepLabV3 | R-101-D8 | 480x480 | 40000 | 9.2 | 7.09 | 46.55 | 47.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context_20200911_204118-1aa27336.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context/deeplabv3_r101-d8_480x480_40k_pascal_context-20200911_204118.log.json) | +| DeepLabV3 | R-101-D8 | 480x480 | 80000 | - | - | 46.42 | 47.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context_20200911_170155-2a21fff3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3/deeplabv3_r101-d8_480x480_80k_pascal_context/deeplabv3_r101-d8_480x480_80k_pascal_context-20200911_170155.log.json) | ### Pascal Context 59 diff --git a/configs/deeplabv3plus/README.md b/configs/deeplabv3plus/README.md index c3320beb438..84ef47effb6 100644 --- a/configs/deeplabv3plus/README.md +++ b/configs/deeplabv3plus/README.md @@ -21,51 +21,51 @@ Note: ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|------------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| DeepLabV3+ | R-50-D8 | 512x1024 | 40000 | 7.5 | 3.94 | 79.61 | 81.01 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610-d222ffcd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610.log.json) | -| DeepLabV3+ | R-101-D8 | 512x1024 | 40000 | 11 | 2.60 | 80.21 | 81.82 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614-3769eecf.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614.log.json) | -| DeepLabV3+ | R-50-D8 | 769x769 | 40000 | 8.5 | 1.72 | 78.97 | 80.46 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143-1dcb0e3c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143.log.json) | -| DeepLabV3+ | R-101-D8 | 769x769 | 40000 | 12.5 | 1.15 | 79.46 | 80.50 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304-ff414b9e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304.log.json) | -| DeepLabV3+ | R-18-D8 | 512x1024 | 80000 | 2.2 | 14.27 | 76.89 | 78.76 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes-20201226_080942.log.json) | -| DeepLabV3+ | R-50-D8 | 512x1024 | 80000 | - | - | 80.09 | 81.13 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049.log.json) | -| DeepLabV3+ | R-101-D8 | 512x1024 | 80000 | - | - | 80.97 | 82.03 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143.log.json) | -| DeepLabV3+ | R-18-D8 | 769x769 | 80000 | 2.5 | 5.74 | 76.26 | 77.91 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes_20201226_083346-f326e06a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes-20201226_083346.log.json) | -| DeepLabV3+ | R-50-D8 | 769x769 | 80000 | - | - | 79.83 | 81.48 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233.log.json) | -| DeepLabV3+ | R-101-D8 | 769x769 | 80000 | - | - | 80.98 | 82.18 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20200607_000405-a7573d20.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20200607_000405.log.json) | -| DeepLabV3+ | R-101-D16-MG124 | 512x1024 | 40000 | 5.8 | 7.48 | 79.09 | 80.36 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes-20200908_005644.log.json) | -| DeepLabV3+ | R-101-D16-MG124 | 512x1024 | 80000 | 9.9 | - | 79.90 | 81.33 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-ee6158e0.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes-20200908_005644.log.json) | -| DeepLabV3+ | R-18b-D8 | 512x1024 | 80000 | 2.1 | 14.95 | 75.87 | 77.52 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes-20201226_090828.log.json) | -| DeepLabV3+ | R-50b-D8 | 512x1024 | 80000 | 7.4 | 3.94 | 80.28 | 81.44 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes_20201225_213645-a97e4e43.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes-20201225_213645.log.json) | -| DeepLabV3+ | R-101b-D8| 512x1024 | 80000 | 10.9 | 2.60 | 80.16 | 81.41 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes_20201226_190843-9c3c93a4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes-20201226_190843.log.json) | -| DeepLabV3+ | R-18b-D8 | 769x769 | 80000 | 2.4 | 5.96 | 76.36 | 78.24 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes_20201226_151312-2c868aff.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes-20201226_151312.log.json) | -| DeepLabV3+ | R-50b-D8 | 769x769 | 80000 | 8.4 | 1.72 | 79.41 | 80.56 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes_20201225_224655-8b596d1c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes-20201225_224655.log.json) | -| DeepLabV3+ | R-101b-D8| 769x769 | 80000 | 12.3 | 1.10 | 79.88 | 81.46 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes_20201226_205041-227cdf7c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes-20201226_205041.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ---------- | --------------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| DeepLabV3+ | R-50-D8 | 512x1024 | 40000 | 7.5 | 3.94 | 79.61 | 81.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610-d222ffcd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_40k_cityscapes/deeplabv3plus_r50-d8_512x1024_40k_cityscapes_20200605_094610.log.json) | +| DeepLabV3+ | R-101-D8 | 512x1024 | 40000 | 11 | 2.60 | 80.21 | 81.82 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614-3769eecf.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_40k_cityscapes/deeplabv3plus_r101-d8_512x1024_40k_cityscapes_20200605_094614.log.json) | +| DeepLabV3+ | R-50-D8 | 769x769 | 40000 | 8.5 | 1.72 | 78.97 | 80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143-1dcb0e3c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes/deeplabv3plus_r50-d8_769x769_40k_cityscapes_20200606_114143.log.json) | +| DeepLabV3+ | R-101-D8 | 769x769 | 40000 | 12.5 | 1.15 | 79.46 | 80.50 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304-ff414b9e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_40k_cityscapes/deeplabv3plus_r101-d8_769x769_40k_cityscapes_20200606_114304.log.json) | +| DeepLabV3+ | R-18-D8 | 512x1024 | 80000 | 2.2 | 14.27 | 76.89 | 78.76 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_512x1024_80k_cityscapes/deeplabv3plus_r18-d8_512x1024_80k_cityscapes-20201226_080942.log.json) | +| DeepLabV3+ | R-50-D8 | 512x1024 | 80000 | - | - | 80.09 | 81.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049-f9fb496d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x1024_80k_cityscapes/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_20200606_114049.log.json) | +| DeepLabV3+ | R-101-D8 | 512x1024 | 80000 | - | - | 80.97 | 82.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143-068fcfe9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_cityscapes_20200606_114143.log.json) | +| DeepLabV3+ | R-18-D8 | 769x769 | 80000 | 2.5 | 5.74 | 76.26 | 77.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes_20201226_083346-f326e06a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18-d8_769x769_80k_cityscapes/deeplabv3plus_r18-d8_769x769_80k_cityscapes-20201226_083346.log.json) | +| DeepLabV3+ | R-50-D8 | 769x769 | 80000 | - | - | 79.83 | 81.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233-0e9dfdc4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_769x769_80k_cityscapes/deeplabv3plus_r50-d8_769x769_80k_cityscapes_20200606_210233.log.json) | +| DeepLabV3+ | R-101-D8 | 769x769 | 80000 | - | - | 80.98 | 82.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20200607_000405-a7573d20.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_769x769_80k_cityscapes/deeplabv3plus_r101-d8_769x769_80k_cityscapes_20200607_000405.log.json) | +| DeepLabV3+ | R-101-D16-MG124 | 512x1024 | 40000 | 5.8 | 7.48 | 79.09 | 80.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes-20200908_005644.log.json) | +| DeepLabV3+ | R-101-D16-MG124 | 512x1024 | 80000 | 9.9 | - | 79.90 | 81.33 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes_20200908_005644-ee6158e0.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes/deeplabv3plus_r101-d16-mg124_512x1024_80k_cityscapes-20200908_005644.log.json) | +| DeepLabV3+ | R-18b-D8 | 512x1024 | 80000 | 2.1 | 14.95 | 75.87 | 77.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes-20201226_090828.log.json) | +| DeepLabV3+ | R-50b-D8 | 512x1024 | 80000 | 7.4 | 3.94 | 80.28 | 81.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes_20201225_213645-a97e4e43.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes/deeplabv3plus_r50b-d8_512x1024_80k_cityscapes-20201225_213645.log.json) | +| DeepLabV3+ | R-101b-D8 | 512x1024 | 80000 | 10.9 | 2.60 | 80.16 | 81.41 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes_20201226_190843-9c3c93a4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes/deeplabv3plus_r101b-d8_512x1024_80k_cityscapes-20201226_190843.log.json) | +| DeepLabV3+ | R-18b-D8 | 769x769 | 80000 | 2.4 | 5.96 | 76.36 | 78.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes_20201226_151312-2c868aff.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r18b-d8_769x769_80k_cityscapes/deeplabv3plus_r18b-d8_769x769_80k_cityscapes-20201226_151312.log.json) | +| DeepLabV3+ | R-50b-D8 | 769x769 | 80000 | 8.4 | 1.72 | 79.41 | 80.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes_20201225_224655-8b596d1c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50b-d8_769x769_80k_cityscapes/deeplabv3plus_r50b-d8_769x769_80k_cityscapes-20201225_224655.log.json) | +| DeepLabV3+ | R-101b-D8 | 769x769 | 80000 | 12.3 | 1.10 | 79.88 | 81.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes_20201226_205041-227cdf7c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101b-d8_769x769_80k_cityscapes/deeplabv3plus_r101b-d8_769x769_80k_cityscapes-20201226_205041.log.json) | ### ADE20K -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|------------|----------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| DeepLabV3+ | R-50-D8 | 512x512 | 80000 | 10.6 | 21.01 | 42.72 | 43.75 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028-bf1400d8.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028.log.json) | -| DeepLabV3+ | R-101-D8 | 512x512 | 80000 | 14.1 | 14.16 | 44.60 | 46.06 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139-d5730af7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139.log.json) | -| DeepLabV3+ | R-50-D8 | 512x512 | 160000 | - | - | 43.95 | 44.93 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504.log.json) | -| DeepLabV3+ | R-101-D8 | 512x512 | 160000 | - | - | 45.47 | 46.35 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| DeepLabV3+ | R-50-D8 | 512x512 | 80000 | 10.6 | 21.01 | 42.72 | 43.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028-bf1400d8.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_80k_ade20k/deeplabv3plus_r50-d8_512x512_80k_ade20k_20200614_185028.log.json) | +| DeepLabV3+ | R-101-D8 | 512x512 | 80000 | 14.1 | 14.16 | 44.60 | 46.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139-d5730af7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_80k_ade20k/deeplabv3plus_r101-d8_512x512_80k_ade20k_20200615_014139.log.json) | +| DeepLabV3+ | R-50-D8 | 512x512 | 160000 | - | - | 43.95 | 44.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504-6135c7e0.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_160k_ade20k/deeplabv3plus_r50-d8_512x512_160k_ade20k_20200615_124504.log.json) | +| DeepLabV3+ | R-101-D8 | 512x512 | 160000 | - | - | 45.47 | 46.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232-38ed86bb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k/deeplabv3plus_r101-d8_512x512_160k_ade20k_20200615_123232.log.json) | #### Pascal VOC 2012 + Aug -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|------------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| DeepLabV3+ | R-50-D8 | 512x512 | 20000 | 7.6 | 21 | 75.93 | 77.50 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323-aad58ef1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323.log.json) | -| DeepLabV3+ | R-101-D8 | 512x512 | 20000 | 11 | 13.88 | 77.22 | 78.59 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345-c7ff3d56.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345.log.json) | -| DeepLabV3+ | R-50-D8 | 512x512 | 40000 | - | - | 76.81 | 77.57 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759-e1b43aa9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759.log.json) | -| DeepLabV3+ | R-101-D8 | 512x512 | 40000 | - | - | 78.62 | 79.53 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| DeepLabV3+ | R-50-D8 | 512x512 | 20000 | 7.6 | 21 | 75.93 | 77.50 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323-aad58ef1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_20k_voc12aug/deeplabv3plus_r50-d8_512x512_20k_voc12aug_20200617_102323.log.json) | +| DeepLabV3+ | R-101-D8 | 512x512 | 20000 | 11 | 13.88 | 77.22 | 78.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345-c7ff3d56.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug/deeplabv3plus_r101-d8_512x512_20k_voc12aug_20200617_102345.log.json) | +| DeepLabV3+ | R-50-D8 | 512x512 | 40000 | - | - | 76.81 | 77.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759-e1b43aa9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r50-d8_512x512_40k_voc12aug/deeplabv3plus_r50-d8_512x512_40k_voc12aug_20200613_161759.log.json) | +| DeepLabV3+ | R-101-D8 | 512x512 | 40000 | - | - | 78.62 | 79.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333-faf03387.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug/deeplabv3plus_r101-d8_512x512_40k_voc12aug_20200613_205333.log.json) | #### Pascal Context -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|------------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| DeepLabV3+ | R-101-D8 | 480x480 | 40000 | - | 9.09 | 47.30 | 48.47 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context_20200911_165459-d3c8a29e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context-20200911_165459.log.json) | -| DeepLabV3+ | R-101-D8 | 480x480 | 80000 | - | - | 47.23 | 48.26 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context_20200911_155322-145d3ee8.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context-20200911_155322.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| DeepLabV3+ | R-101-D8 | 480x480 | 40000 | - | 9.09 | 47.30 | 48.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context_20200911_165459-d3c8a29e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_40k_pascal_context/deeplabv3plus_r101-d8_480x480_40k_pascal_context-20200911_165459.log.json) | +| DeepLabV3+ | R-101-D8 | 480x480 | 80000 | - | - | 47.23 | 48.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context_20200911_155322-145d3ee8.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context/deeplabv3plus_r101-d8_480x480_80k_pascal_context-20200911_155322.log.json) | #### Pascal Context 59 diff --git a/configs/dmnet/README.md b/configs/dmnet/README.md index 9b12c8d862f..3f3653aa9cd 100644 --- a/configs/dmnet/README.md +++ b/configs/dmnet/README.md @@ -18,22 +18,22 @@ year = {2019} ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| DMNet | R-50-D8 | 512x1024 | 40000 | 7.0 | 3.66 | 77.78 | 79.14 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes-20201214_115717.log.json) | -| DMNet | R-101-D8 | 512x1024 | 40000 | 10.6 | 2.54 | 78.37 | 79.72 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes-20201214_115716.log.json) | -| DMNet | R-50-D8 | 769x769 | 40000 | 7.9 | 1.57 | 78.49 | 80.27 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes-20201214_115717.log.json) | -| DMNet | R-101-D8 | 769x769 | 40000 | 12.0 | 1.01 | 77.62 | 78.94 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes-20201214_115718.log.json) | -| DMNet | R-50-D8 | 512x1024 | 80000 | - | - | 79.07 | 80.22 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes-20201214_115716.log.json) | -| DMNet | R-101-D8 | 512x1024 | 80000 | - | - | 79.64 | 80.67 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes-20201214_115705.log.json) | -| DMNet | R-50-D8 | 769x769 | 80000 | - | - | 79.22 | 80.55 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes-20201214_115718.log.json) | -| DMNet | R-101-D8 | 769x769 | 80000 | - | - | 79.19 | 80.65 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes-20201214_115716.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| DMNet | R-50-D8 | 512x1024 | 40000 | 7.0 | 3.66 | 77.78 | 79.14 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes_20201214_115717-5e88fa33.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes-20201214_115717.log.json) | +| DMNet | R-101-D8 | 512x1024 | 40000 | 10.6 | 2.54 | 78.37 | 79.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes_20201214_115716-abc9d111.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes-20201214_115716.log.json) | +| DMNet | R-50-D8 | 769x769 | 40000 | 7.9 | 1.57 | 78.49 | 80.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes_20201214_115717-2a2628d7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes-20201214_115717.log.json) | +| DMNet | R-101-D8 | 769x769 | 40000 | 12.0 | 1.01 | 77.62 | 78.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes_20201214_115718-b650de90.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes-20201214_115718.log.json) | +| DMNet | R-50-D8 | 512x1024 | 80000 | - | - | 79.07 | 80.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes_20201214_115716-987f51e3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes-20201214_115716.log.json) | +| DMNet | R-101-D8 | 512x1024 | 80000 | - | - | 79.64 | 80.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes_20201214_115705-b1ff208a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes-20201214_115705.log.json) | +| DMNet | R-50-D8 | 769x769 | 80000 | - | - | 79.22 | 80.55 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes_20201214_115718-7ea9fa12.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes-20201214_115718.log.json) | +| DMNet | R-101-D8 | 769x769 | 80000 | - | - | 79.19 | 80.65 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes_20201214_115716-a7fbc2ab.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes-20201214_115716.log.json) | ### ADE20K -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| DMNet | R-50-D8 | 512x512 | 80000 | 9.4 | 20.95 | 42.37 | 43.62 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k-20201214_115705.log.json) | -| DMNet | R-101-D8 | 512x512 | 80000 | 13.0 | 13.88 | 45.34 | 46.13 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k-20201214_115704.log.json) | -| DMNet | R-50-D8 | 512x512 | 160000 | - | - | 43.15 | 44.17 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k-20201214_115706.log.json) | -| DMNet | R-101-D8 | 512x512 | 160000 | - | - | 45.42 | 46.76 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k-20201214_115705.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| DMNet | R-50-D8 | 512x512 | 80000 | 9.4 | 20.95 | 42.37 | 43.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k_20201214_115705-a8626293.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k-20201214_115705.log.json) | +| DMNet | R-101-D8 | 512x512 | 80000 | 13.0 | 13.88 | 45.34 | 46.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k_20201214_115704-c656c3fb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k-20201214_115704.log.json) | +| DMNet | R-50-D8 | 512x512 | 160000 | - | - | 43.15 | 44.17 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k_20201214_115706-25fb92c2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k-20201214_115706.log.json) | +| DMNet | R-101-D8 | 512x512 | 160000 | - | - | 45.42 | 46.76 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k_20201214_115705-73f9a8d7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k-20201214_115705.log.json) | diff --git a/configs/dnlnet/README.md b/configs/dnlnet/README.md index 172dfe1a0f0..fe99c4b7c5f 100644 --- a/configs/dnlnet/README.md +++ b/configs/dnlnet/README.md @@ -21,22 +21,22 @@ This example is to reproduce ["Disentangled Non-Local Neural Networks"](https:// ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|---------:|----------------|------:|---------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| dnl | R-50-D8 | 512x1024 | 40000 | 7.3 | 2.56 | 78.61 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes_20200904_233629-53d4ea93.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes-20200904_233629.log.json) | -| dnl | R-101-D8 | 512x1024 | 40000 | 10.9 | 1.96 | 78.31 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes_20200904_233629-9928ffef.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes-20200904_233629.log.json) | -| dnl | R-50-D8 | 769x769 | 40000 | 9.2 | 1.50 | 78.44 | 80.27 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes_20200820_232206-0f283785.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes-20200820_232206.log.json) | -| dnl | R-101-D8 | 769x769 | 40000 | 12.6 | 1.02 | 76.39 | 77.77 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes_20200820_171256-76c596df.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes-20200820_171256.log.json) | -| dnl | R-50-D8 | 512x1024 | 80000 | - | - | 79.33 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes_20200904_233629-58b2f778.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes-20200904_233629.log.json) | -| dnl | R-101-D8 | 512x1024 | 80000 | - | - | 80.41 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes_20200904_233629-758e2dd4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes-20200904_233629.log.json) | -| dnl | R-50-D8 | 769x769 | 80000 | - | - | 79.36 | 80.70 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes_20200820_011925-366bc4c7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes-20200820_011925.log.json) | -| dnl | R-101-D8 | 769x769 | 80000 | - | - | 79.41 | 80.68 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes_20200821_051111-95ff84ab.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes-20200821_051111.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| dnl | R-50-D8 | 512x1024 | 40000 | 7.3 | 2.56 | 78.61 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes_20200904_233629-53d4ea93.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_40k_cityscapes/dnl_r50-d8_512x1024_40k_cityscapes-20200904_233629.log.json) | +| dnl | R-101-D8 | 512x1024 | 40000 | 10.9 | 1.96 | 78.31 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes_20200904_233629-9928ffef.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_40k_cityscapes/dnl_r101-d8_512x1024_40k_cityscapes-20200904_233629.log.json) | +| dnl | R-50-D8 | 769x769 | 40000 | 9.2 | 1.50 | 78.44 | 80.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes_20200820_232206-0f283785.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_40k_cityscapes/dnl_r50-d8_769x769_40k_cityscapes-20200820_232206.log.json) | +| dnl | R-101-D8 | 769x769 | 40000 | 12.6 | 1.02 | 76.39 | 77.77 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes_20200820_171256-76c596df.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_40k_cityscapes/dnl_r101-d8_769x769_40k_cityscapes-20200820_171256.log.json) | +| dnl | R-50-D8 | 512x1024 | 80000 | - | - | 79.33 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes_20200904_233629-58b2f778.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x1024_80k_cityscapes/dnl_r50-d8_512x1024_80k_cityscapes-20200904_233629.log.json) | +| dnl | R-101-D8 | 512x1024 | 80000 | - | - | 80.41 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes_20200904_233629-758e2dd4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x1024_80k_cityscapes/dnl_r101-d8_512x1024_80k_cityscapes-20200904_233629.log.json) | +| dnl | R-50-D8 | 769x769 | 80000 | - | - | 79.36 | 80.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes_20200820_011925-366bc4c7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_769x769_80k_cityscapes/dnl_r50-d8_769x769_80k_cityscapes-20200820_011925.log.json) | +| dnl | R-101-D8 | 769x769 | 80000 | - | - | 79.41 | 80.68 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes_20200821_051111-95ff84ab.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_769x769_80k_cityscapes/dnl_r101-d8_769x769_80k_cityscapes-20200821_051111.log.json) | ### ADE20K -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|---------:|----------------|------:|---------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| DNL | R-50-D8 | 512x512 | 80000 | 8.8 | 20.66 | 41.76 | 42.99 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k_20200826_183354-1cf6e0c1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k-20200826_183354.log.json) | -| DNL | R-101-D8 | 512x512 | 80000 | 12.8 | 12.54 | 43.76 | 44.91 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k_20200826_183354-d820d6ea.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k-20200826_183354.log.json) | -| DNL | R-50-D8 | 512x512 | 160000 | - | - | 41.87 | 43.01 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k_20200826_183350-37837798.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k-20200826_183350.log.json) | -| DNL | R-101-D8 | 512x512 | 160000 | - | - | 44.25 | 45.78 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k_20200826_183350-ed522c61.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k-20200826_183350.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | -------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| DNL | R-50-D8 | 512x512 | 80000 | 8.8 | 20.66 | 41.76 | 42.99 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k_20200826_183354-1cf6e0c1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_80k_ade20k/dnl_r50-d8_512x512_80k_ade20k-20200826_183354.log.json) | +| DNL | R-101-D8 | 512x512 | 80000 | 12.8 | 12.54 | 43.76 | 44.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k_20200826_183354-d820d6ea.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_80k_ade20k/dnl_r101-d8_512x512_80k_ade20k-20200826_183354.log.json) | +| DNL | R-50-D8 | 512x512 | 160000 | - | - | 41.87 | 43.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k_20200826_183350-37837798.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r50-d8_512x512_160k_ade20k/dnl_r50-d8_512x512_160k_ade20k-20200826_183350.log.json) | +| DNL | R-101-D8 | 512x512 | 160000 | - | - | 44.25 | 45.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet/dnl_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k_20200826_183350-ed522c61.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/dnlnet/dnl_r101-d8_512x512_160k_ade20k/dnl_r101-d8_512x512_160k_ade20k-20200826_183350.log.json) | diff --git a/configs/emanet/README.md b/configs/emanet/README.md index 40df946ed44..615d2a7b2b1 100644 --- a/configs/emanet/README.md +++ b/configs/emanet/README.md @@ -18,9 +18,9 @@ ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|---------:|----------------|------:|---------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| EMANet | R-50-D8 | 512x1024 | 80000 | 5.4 | 4.58 | 77.59 | 79.44 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes_20200901_100301-c43fcef1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes-20200901_100301.log.json) | -| EMANet | R-101-D8 | 512x1024 | 80000 | 6.2 | 2.87 | 79.10 | 81.21 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes_20200901_100301-2d970745.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes-20200901_100301.log.json) | -| EMANet | R-50-D8 | 769x769 | 80000 | 8.9 | 1.97 | 79.33 | 80.49 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes_20200901_100301-16f8de52.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes-20200901_100301.log.json) | -| EMANet | R-101-D8 | 769x769 | 80000 | 10.1 | 1.22 | 79.62 | 81.00 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes_20200901_100301-47a324ce.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes-20200901_100301.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| EMANet | R-50-D8 | 512x1024 | 80000 | 5.4 | 4.58 | 77.59 | 79.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes_20200901_100301-c43fcef1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes-20200901_100301.log.json) | +| EMANet | R-101-D8 | 512x1024 | 80000 | 6.2 | 2.87 | 79.10 | 81.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes_20200901_100301-2d970745.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes-20200901_100301.log.json) | +| EMANet | R-50-D8 | 769x769 | 80000 | 8.9 | 1.97 | 79.33 | 80.49 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes_20200901_100301-16f8de52.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes-20200901_100301.log.json) | +| EMANet | R-101-D8 | 769x769 | 80000 | 10.1 | 1.22 | 79.62 | 81.00 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes_20200901_100301-47a324ce.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes-20200901_100301.log.json) | diff --git a/configs/encnet/README.md b/configs/encnet/README.md index 6ba42f69fae..12ab656da29 100644 --- a/configs/encnet/README.md +++ b/configs/encnet/README.md @@ -18,22 +18,22 @@ year = {2018} ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| encnet | R-50-D8 | 512x1024 | 40000 | 8.6 | 4.58 | 75.67 | 77.08 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes_20200621_220958-68638a47.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes-20200621_220958.log.json) | -| encnet | R-101-D8 | 512x1024 | 40000 | 12.1 | 2.66 | 75.81 | 77.21 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes_20200621_220933-35e0a3e8.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes-20200621_220933.log.json) | -| encnet | R-50-D8 | 769x769 | 40000 | 9.8 | 1.82 | 76.24 | 77.85 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes_20200621_220958-3bcd2884.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes-20200621_220958.log.json) | -| encnet | R-101-D8 | 769x769 | 40000 | 13.7 | 1.26 | 74.25 | 76.25 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes_20200621_220933-2fafed55.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes-20200621_220933.log.json) | -| encnet | R-50-D8 | 512x1024 | 80000 | - | - | 77.94 | 79.13 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes_20200622_003554-fc5c5624.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes-20200622_003554.log.json) | -| encnet | R-101-D8 | 512x1024 | 80000 | - | - | 78.55 | 79.47 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes_20200622_003555-1de64bec.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes-20200622_003555.log.json) | -| encnet | R-50-D8 | 769x769 | 80000 | - | - | 77.44 | 78.72 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes_20200622_003554-55096dcb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes-20200622_003554.log.json) | -| encnet | R-101-D8 | 769x769 | 80000 | - | - | 76.10 | 76.97 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes_20200622_003555-470ef79d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes-20200622_003555.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| encnet | R-50-D8 | 512x1024 | 40000 | 8.6 | 4.58 | 75.67 | 77.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes_20200621_220958-68638a47.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_40k_cityscapes/encnet_r50-d8_512x1024_40k_cityscapes-20200621_220958.log.json) | +| encnet | R-101-D8 | 512x1024 | 40000 | 12.1 | 2.66 | 75.81 | 77.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes_20200621_220933-35e0a3e8.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_40k_cityscapes/encnet_r101-d8_512x1024_40k_cityscapes-20200621_220933.log.json) | +| encnet | R-50-D8 | 769x769 | 40000 | 9.8 | 1.82 | 76.24 | 77.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes_20200621_220958-3bcd2884.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_40k_cityscapes/encnet_r50-d8_769x769_40k_cityscapes-20200621_220958.log.json) | +| encnet | R-101-D8 | 769x769 | 40000 | 13.7 | 1.26 | 74.25 | 76.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes_20200621_220933-2fafed55.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_40k_cityscapes/encnet_r101-d8_769x769_40k_cityscapes-20200621_220933.log.json) | +| encnet | R-50-D8 | 512x1024 | 80000 | - | - | 77.94 | 79.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes_20200622_003554-fc5c5624.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x1024_80k_cityscapes/encnet_r50-d8_512x1024_80k_cityscapes-20200622_003554.log.json) | +| encnet | R-101-D8 | 512x1024 | 80000 | - | - | 78.55 | 79.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes_20200622_003555-1de64bec.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x1024_80k_cityscapes/encnet_r101-d8_512x1024_80k_cityscapes-20200622_003555.log.json) | +| encnet | R-50-D8 | 769x769 | 80000 | - | - | 77.44 | 78.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes_20200622_003554-55096dcb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_769x769_80k_cityscapes/encnet_r50-d8_769x769_80k_cityscapes-20200622_003554.log.json) | +| encnet | R-101-D8 | 769x769 | 80000 | - | - | 76.10 | 76.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes_20200622_003555-470ef79d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_769x769_80k_cityscapes/encnet_r101-d8_769x769_80k_cityscapes-20200622_003555.log.json) | ### ADE20K -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| encnet | R-50-D8 | 512x512 | 80000 | 10.1 | 22.81 | 39.53 | 41.17 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k_20200622_042412-44b46b04.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k-20200622_042412.log.json) | -| encnet | R-101-D8 | 512x512 | 80000 | 13.6 | 14.87 | 42.11 | 43.61 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k_20200622_101128-dd35e237.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k-20200622_101128.log.json) | -| encnet | R-50-D8 | 512x512 | 160000 | - | - | 40.10 | 41.71 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k_20200622_101059-b2db95e0.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k-20200622_101059.log.json) | -| encnet | R-101-D8 | 512x512 | 160000 | - | - | 42.61 | 44.01 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k_20200622_073348-7989641f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k-20200622_073348.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| encnet | R-50-D8 | 512x512 | 80000 | 10.1 | 22.81 | 39.53 | 41.17 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k_20200622_042412-44b46b04.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_80k_ade20k/encnet_r50-d8_512x512_80k_ade20k-20200622_042412.log.json) | +| encnet | R-101-D8 | 512x512 | 80000 | 13.6 | 14.87 | 42.11 | 43.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k_20200622_101128-dd35e237.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_80k_ade20k/encnet_r101-d8_512x512_80k_ade20k-20200622_101128.log.json) | +| encnet | R-50-D8 | 512x512 | 160000 | - | - | 40.10 | 41.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k_20200622_101059-b2db95e0.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r50-d8_512x512_160k_ade20k/encnet_r50-d8_512x512_160k_ade20k-20200622_101059.log.json) | +| encnet | R-101-D8 | 512x512 | 160000 | - | - | 42.61 | 44.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet/encnet_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k_20200622_073348-7989641f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/encnet/encnet_r101-d8_512x512_160k_ade20k/encnet_r101-d8_512x512_160k_ade20k-20200622_073348.log.json) | diff --git a/configs/fastscnn/README.md b/configs/fastscnn/README.md index bb87a9f7aeb..8dae279b96f 100644 --- a/configs/fastscnn/README.md +++ b/configs/fastscnn/README.md @@ -17,6 +17,6 @@ ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|------------|-----------|-----------|--------:|----------|----------------|------:|---------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| Fast-SCNN | Fast-SCNN | 512x1024 | 80000 | 8.4 | 63.61 | 69.06 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_4x8_80k_lr0.12_cityscapes-f5096c79.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_4x8_80k_lr0.12_cityscapes-20200807_165744.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| --------- | --------- | --------- | ------: | -------- | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| Fast-SCNN | Fast-SCNN | 512x1024 | 80000 | 8.4 | 63.61 | 69.06 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fast_scnn.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_4x8_80k_lr0.12_cityscapes-f5096c79.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fast_scnn/fast_scnn_4x8_80k_lr0.12_cityscapes-20200807_165744.log.json) | diff --git a/configs/fcn/README.md b/configs/fcn/README.md index 022eee91fea..851e387a84c 100644 --- a/configs/fcn/README.md +++ b/configs/fcn/README.md @@ -21,61 +21,61 @@ ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| FCN | R-50-D8 | 512x1024 | 40000 | 5.7 | 4.17 | 72.25 | 73.36 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608.log.json) | -| FCN | R-101-D8 | 512x1024 | 40000 | 9.2 | 2.66 | 75.45 | 76.58 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852-a883d3a1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852.log.json) | -| FCN | R-50-D8 | 769x769 | 40000 | 6.5 | 1.80 | 71.47 | 72.54 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104-977b5d02.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104.log.json) | -| FCN | R-101-D8 | 769x769 | 40000 | 10.4 | 1.19 | 73.93 | 75.14 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208-7d4ab69c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208.log.json) | -| FCN | R-18-D8 | 512x1024 | 80000 | 1.7 | 14.65 | 71.11 | 72.91 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes-20201225_021327.log.json) | -| FCN | R-50-D8 | 512x1024 | 80000 | - | | 73.61 | 74.24 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019-03aa804d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019.log.json) | -| FCN | R-101-D8 | 512x1024 | 80000 | - | - | 75.13 | 75.94 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038-3fb937eb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038.log.json) | -| FCN | R-18-D8 | 769x769 | 80000 | 1.9 | 6.40 | 70.80 | 73.16 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes_20201225_021451-9739d1b8.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes-20201225_021451.log.json) | -| FCN | R-50-D8 | 769x769 | 80000 | - | - | 72.64 | 73.32 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749-f5caeabc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749.log.json) | -| FCN | R-101-D8 | 769x769 | 80000 | - | - | 75.52 | 76.61 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354-45cbac68.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354.log.json) | -| FCN | R-18b-D8 | 512x1024 | 80000 | 1.6 | 16.74 | 70.24 | 72.77 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes-20201225_230143.log.json) | -| FCN | R-50b-D8 | 512x1024 | 80000 | 5.6 | 4.20 | 75.65 | 77.59 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes_20201225_094221-82957416.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes-20201225_094221.log.json) | -| FCN | R-101b-D8| 512x1024 | 80000 | 9.1 | 2.73 | 77.37 | 78.77 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes_20201226_160213-4543858f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes-20201226_160213.log.json) | -| FCN | R-18b-D8 | 769x769 | 80000 | 1.7 | 6.70 | 69.66 | 72.07 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes_20201226_004430-32d504e5.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes-20201226_004430.log.json) | -| FCN | R-50b-D8 | 769x769 | 80000 | 6.3 | 1.82 | 73.83 | 76.60 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes_20201225_094223-94552d38.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes-20201225_094223.log.json) | -| FCN | R-101b-D8| 769x769 | 80000 | 10.3 | 1.15 | 77.02 | 78.67 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes_20201226_170012-82be37e2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes-20201226_170012.log.json) | -|FCN-D6|R-50-D16|512x1024|40000|3.4|10.22|77.06|78.85| [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-20210305_130133.log.json)| -|FCN-D6|R-50-D16|512x1024|80000|-|10.35|77.27|78.88| [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_80k_cityscapes-20210306_115604.log.json) | -|FCN-D6|R-50-D16|769x769|40000|3.7|4.17|76.82|78.22| [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-1aab18ed.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-20210305_185744.log.json) | -|FCN-D6|R-50-D16|769x769|80000|-|4.15|77.04|78.40| [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-109d88eb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-20210305_200413.log.json) | -|FCN-D6|R-101-D16|512x1024|40000|4.5|8.04|77.36|79.18| [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-9cf2b450.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-20210305_130337.log.json) | -|FCN-D6|R-101-D16|512x1024|80000|-|8.26|78.46|80.42| [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-cb336445.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-20210308_102747.log.json) | -|FCN-D6|R-101-D16|769x769|40000|5.0|3.12|77.28|78.95| [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-60b114e9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-20210308_102453.log.json) | -|FCN-D6|R-101-D16|769x769|80000|-|3.21|78.06|79.58| [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-e33adc4f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-20210306_120016.log.json) | -|FCN-D6|R-50b-D16|512x1024|80000|3.2|10.16|76.99|79.03| [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes/fcn_d6_r50b_d16_512x1024_80k_cityscapes-6a0b62e9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes/fcn_d6_r50b_d16_512x1024_80k_cityscapes-20210311_125550.log.json) | -|FCN-D6|R-50b-D16|769x769|80000|3.6|4.17|76.86|78.52| [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes/fcn_d6_r50b_d16_769x769_80k_cityscapes-d665f231.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes/fcn_d6_r50b_d16_769x769_80k_cityscapes-20210311_131012.log.json) | -|FCN-D6|R-101b-D16|512x1024|80000|4.3|8.46|77.72|79.53| [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes/fcn_d6_r101b_d16_512x1024_80k_cityscapes-3f2eb5b4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes/fcn_d6_r101b_d16_512x1024_80k_cityscapes-20210311_144305.log.json) | -|FCN-D6|R-101b-D16|769x769|80000|4.8|3.32|77.34|78.91| [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes/fcn_d6_r101b_d16_769x769_80k_cityscapes-c4d8bfbc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes/fcn_d6_r101b_d16_769x769_80k_cityscapes-20210311_154527.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | ---------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| FCN | R-50-D8 | 512x1024 | 40000 | 5.7 | 4.17 | 72.25 | 73.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608.log.json) | +| FCN | R-101-D8 | 512x1024 | 40000 | 9.2 | 2.66 | 75.45 | 76.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852-a883d3a1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_40k_cityscapes/fcn_r101-d8_512x1024_40k_cityscapes_20200604_181852.log.json) | +| FCN | R-50-D8 | 769x769 | 40000 | 6.5 | 1.80 | 71.47 | 72.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104-977b5d02.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_40k_cityscapes/fcn_r50-d8_769x769_40k_cityscapes_20200606_113104.log.json) | +| FCN | R-101-D8 | 769x769 | 40000 | 10.4 | 1.19 | 73.93 | 75.14 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208-7d4ab69c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_40k_cityscapes/fcn_r101-d8_769x769_40k_cityscapes_20200606_113208.log.json) | +| FCN | R-18-D8 | 512x1024 | 80000 | 1.7 | 14.65 | 71.11 | 72.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_512x1024_80k_cityscapes/fcn_r18-d8_512x1024_80k_cityscapes-20201225_021327.log.json) | +| FCN | R-50-D8 | 512x1024 | 80000 | - | | 73.61 | 74.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019-03aa804d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_80k_cityscapes/fcn_r50-d8_512x1024_80k_cityscapes_20200606_113019.log.json) | +| FCN | R-101-D8 | 512x1024 | 80000 | - | - | 75.13 | 75.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038-3fb937eb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x1024_80k_cityscapes/fcn_r101-d8_512x1024_80k_cityscapes_20200606_113038.log.json) | +| FCN | R-18-D8 | 769x769 | 80000 | 1.9 | 6.40 | 70.80 | 73.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r18-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes_20201225_021451-9739d1b8.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18-d8_769x769_80k_cityscapes/fcn_r18-d8_769x769_80k_cityscapes-20201225_021451.log.json) | +| FCN | R-50-D8 | 769x769 | 80000 | - | - | 72.64 | 73.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749-f5caeabc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_769x769_80k_cityscapes/fcn_r50-d8_769x769_80k_cityscapes_20200606_195749.log.json) | +| FCN | R-101-D8 | 769x769 | 80000 | - | - | 75.52 | 76.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354-45cbac68.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_769x769_80k_cityscapes/fcn_r101-d8_769x769_80k_cityscapes_20200606_214354.log.json) | +| FCN | R-18b-D8 | 512x1024 | 80000 | 1.6 | 16.74 | 70.24 | 72.77 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_512x1024_80k_cityscapes/fcn_r18b-d8_512x1024_80k_cityscapes-20201225_230143.log.json) | +| FCN | R-50b-D8 | 512x1024 | 80000 | 5.6 | 4.20 | 75.65 | 77.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes_20201225_094221-82957416.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_512x1024_80k_cityscapes/fcn_r50b-d8_512x1024_80k_cityscapes-20201225_094221.log.json) | +| FCN | R-101b-D8 | 512x1024 | 80000 | 9.1 | 2.73 | 77.37 | 78.77 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes_20201226_160213-4543858f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_512x1024_80k_cityscapes/fcn_r101b-d8_512x1024_80k_cityscapes-20201226_160213.log.json) | +| FCN | R-18b-D8 | 769x769 | 80000 | 1.7 | 6.70 | 69.66 | 72.07 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes_20201226_004430-32d504e5.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r18b-d8_769x769_80k_cityscapes/fcn_r18b-d8_769x769_80k_cityscapes-20201226_004430.log.json) | +| FCN | R-50b-D8 | 769x769 | 80000 | 6.3 | 1.82 | 73.83 | 76.60 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes_20201225_094223-94552d38.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50b-d8_769x769_80k_cityscapes/fcn_r50b-d8_769x769_80k_cityscapes-20201225_094223.log.json) | +| FCN | R-101b-D8 | 769x769 | 80000 | 10.3 | 1.15 | 77.02 | 78.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes_20201226_170012-82be37e2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101b-d8_769x769_80k_cityscapes/fcn_r101b-d8_769x769_80k_cityscapes-20201226_170012.log.json) | +| FCN-D6 | R-50-D16 | 512x1024 | 40000 | 3.4 | 10.22 | 77.06 | 78.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_40k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-20210305_130133.log.json) | +| FCN-D6 | R-50-D16 | 512x1024 | 80000 | - | 10.35 | 77.27 | 78.88 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_40k_cityscapes-98d5d1bc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_512x1024_80k_cityscapes/fcn_d6_r50-d16_512x1024_80k_cityscapes-20210306_115604.log.json) | +| FCN-D6 | R-50-D16 | 769x769 | 40000 | 3.7 | 4.17 | 76.82 | 78.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-1aab18ed.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_40k_cityscapes/fcn_d6_r50-d16_769x769_40k_cityscapes-20210305_185744.log.json) | +| FCN-D6 | R-50-D16 | 769x769 | 80000 | - | 4.15 | 77.04 | 78.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-109d88eb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50-d16_769x769_80k_cityscapes/fcn_d6_r50-d16_769x769_80k_cityscapes-20210305_200413.log.json) | +| FCN-D6 | R-101-D16 | 512x1024 | 40000 | 4.5 | 8.04 | 77.36 | 79.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-9cf2b450.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_40k_cityscapes/fcn_d6_r101-d16_512x1024_40k_cityscapes-20210305_130337.log.json) | +| FCN-D6 | R-101-D16 | 512x1024 | 80000 | - | 8.26 | 78.46 | 80.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-cb336445.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_512x1024_80k_cityscapes/fcn_d6_r101-d16_512x1024_80k_cityscapes-20210308_102747.log.json) | +| FCN-D6 | R-101-D16 | 769x769 | 40000 | 5.0 | 3.12 | 77.28 | 78.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-60b114e9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_40k_cityscapes/fcn_d6_r101-d16_769x769_40k_cityscapes-20210308_102453.log.json) | +| FCN-D6 | R-101-D16 | 769x769 | 80000 | - | 3.21 | 78.06 | 79.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-e33adc4f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes/fcn_d6_r101-d16_769x769_80k_cityscapes-20210306_120016.log.json) | +| FCN-D6 | R-50b-D16 | 512x1024 | 80000 | 3.2 | 10.16 | 76.99 | 79.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes/fcn_d6_r50b_d16_512x1024_80k_cityscapes-6a0b62e9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_512x1024_80k_cityscapes/fcn_d6_r50b_d16_512x1024_80k_cityscapes-20210311_125550.log.json) | +| FCN-D6 | R-50b-D16 | 769x769 | 80000 | 3.6 | 4.17 | 76.86 | 78.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes/fcn_d6_r50b_d16_769x769_80k_cityscapes-d665f231.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r50b_d16_769x769_80k_cityscapes/fcn_d6_r50b_d16_769x769_80k_cityscapes-20210311_131012.log.json) | +| FCN-D6 | R-101b-D16 | 512x1024 | 80000 | 4.3 | 8.46 | 77.72 | 79.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes/fcn_d6_r101b_d16_512x1024_80k_cityscapes-3f2eb5b4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_512x1024_80k_cityscapes/fcn_d6_r101b_d16_512x1024_80k_cityscapes-20210311_144305.log.json) | +| FCN-D6 | R-101b-D16 | 769x769 | 80000 | 4.8 | 3.32 | 77.34 | 78.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes/fcn_d6_r101b_d16_769x769_80k_cityscapes-c4d8bfbc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_d6_r101b_d16_769x769_80k_cityscapes/fcn_d6_r101b_d16_769x769_80k_cityscapes-20210311_154527.log.json) | ### ADE20K -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| FCN | R-50-D8 | 512x512 | 80000 | 8.5 | 23.49 | 35.94 | 37.94 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016-f8ac5082.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016.log.json) | -| FCN | R-101-D8 | 512x512 | 80000 | 12 | 14.78 | 39.61 | 40.83 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143-bc1809f7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143.log.json) | -| FCN | R-50-D8 | 512x512 | 160000 | - | - | 36.10 | 38.08 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713-4edbc3b4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713.log.json) | -| FCN | R-101-D8 | 512x512 | 160000 | - | - | 39.91 | 41.40 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816-fd192bd5.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| FCN | R-50-D8 | 512x512 | 80000 | 8.5 | 23.49 | 35.94 | 37.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016-f8ac5082.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_80k_ade20k/fcn_r50-d8_512x512_80k_ade20k_20200614_144016.log.json) | +| FCN | R-101-D8 | 512x512 | 80000 | 12 | 14.78 | 39.61 | 40.83 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143-bc1809f7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_80k_ade20k/fcn_r101-d8_512x512_80k_ade20k_20200615_014143.log.json) | +| FCN | R-50-D8 | 512x512 | 160000 | - | - | 36.10 | 38.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713-4edbc3b4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_160k_ade20k/fcn_r50-d8_512x512_160k_ade20k_20200615_100713.log.json) | +| FCN | R-101-D8 | 512x512 | 160000 | - | - | 39.91 | 41.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816-fd192bd5.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_160k_ade20k/fcn_r101-d8_512x512_160k_ade20k_20200615_105816.log.json) | ### Pascal VOC 2012 + Aug -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| FCN | R-50-D8 | 512x512 | 20000 | 5.7 | 23.28 | 67.08 | 69.94 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715-52dc5306.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715.log.json) | -| FCN | R-101-D8 | 512x512 | 20000 | 9.2 | 14.81 | 71.16 | 73.57 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842-0bb4e798.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842.log.json) | -| FCN | R-50-D8 | 512x512 | 40000 | - | - | 66.97 | 69.04 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222-5e2dbf40.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222.log.json) | -| FCN | R-101-D8 | 512x512 | 40000 | - | - | 69.91 | 72.38 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240-4c8bcefd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| FCN | R-50-D8 | 512x512 | 20000 | 5.7 | 23.28 | 67.08 | 69.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715-52dc5306.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_20k_voc12aug/fcn_r50-d8_512x512_20k_voc12aug_20200617_010715.log.json) | +| FCN | R-101-D8 | 512x512 | 20000 | 9.2 | 14.81 | 71.16 | 73.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842-0bb4e798.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_20k_voc12aug/fcn_r101-d8_512x512_20k_voc12aug_20200617_010842.log.json) | +| FCN | R-50-D8 | 512x512 | 40000 | - | - | 66.97 | 69.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222-5e2dbf40.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x512_40k_voc12aug/fcn_r50-d8_512x512_40k_voc12aug_20200613_161222.log.json) | +| FCN | R-101-D8 | 512x512 | 40000 | - | - | 69.91 | 72.38 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240-4c8bcefd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_512x512_40k_voc12aug/fcn_r101-d8_512x512_40k_voc12aug_20200613_161240.log.json) | ### Pascal Context -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| FCN | R-101-D8 | 480x480 | 40000 | - | 9.93 | 44.14 | 45.67 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context_20200911_212515-9b565a6d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context-20200911_212515.log.json) | -| FCN | R-101-D8 | 480x480 | 80000 | - | - | 44.47 | 45.74 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context_20200915_032644-a3828480.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context-20200915_032644.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| FCN | R-101-D8 | 480x480 | 40000 | - | 9.93 | 44.14 | 45.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_480x480_40k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context_20200911_212515-9b565a6d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_40k_pascal_context/fcn_r101-d8_480x480_40k_pascal_context-20200911_212515.log.json) | +| FCN | R-101-D8 | 480x480 | 80000 | - | - | 44.47 | 45.74 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn/fcn_r101-d8_480x480_80k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context_20200915_032644-a3828480.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r101-d8_480x480_80k_pascal_context/fcn_r101-d8_480x480_80k_pascal_context-20200915_032644.log.json) | ### Pascal Context 59 diff --git a/configs/fp16/README.md b/configs/fp16/README.md index 8d12e4d7802..40ee750f23e 100644 --- a/configs/fp16/README.md +++ b/configs/fp16/README.md @@ -17,9 +17,9 @@ ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| FCN | R-101-D8 | 512x1024 | 80000 | 5.50 | 2.66 | 76.80 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes/fcn_r101-d8_512x1024_80k_fp16_cityscapes-50245227.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes/fcn_r101-d8_512x1024_80k_fp16_cityscapes_20200717_230921.log.json) | -| PSPNet | R-101-D8 | 512x1024 | 80000 | 5.47 | 2.68 | 79.46 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes/pspnet_r101-d8_512x1024_80k_fp16_cityscapes-ade37931.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes/pspnet_r101-d8_512x1024_80k_fp16_cityscapes_20200717_230919.log.json) | -| DeepLabV3 | R-101-D8 | 512x1024 | 80000 | 5.91 | 1.93 | 80.48 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes-bc86dc84.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes_20200717_230920.log.json) | -| DeepLabV3+ | R-101-D8 | 512x1024 | 80000 | 6.46 | 2.60 | 80.46 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes-cc58bc8d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes_20200717_230920.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ---------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| FCN | R-101-D8 | 512x1024 | 80000 | 5.50 | 2.66 | 76.80 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes/fcn_r101-d8_512x1024_80k_fp16_cityscapes-50245227.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/fcn_r101-d8_512x1024_80k_fp16_cityscapes/fcn_r101-d8_512x1024_80k_fp16_cityscapes_20200717_230921.log.json) | +| PSPNet | R-101-D8 | 512x1024 | 80000 | 5.47 | 2.68 | 79.46 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes/pspnet_r101-d8_512x1024_80k_fp16_cityscapes-ade37931.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/pspnet_r101-d8_512x1024_80k_fp16_cityscapes/pspnet_r101-d8_512x1024_80k_fp16_cityscapes_20200717_230919.log.json) | +| DeepLabV3 | R-101-D8 | 512x1024 | 80000 | 5.91 | 1.93 | 80.48 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes-bc86dc84.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3_r101-d8_512x1024_80k_fp16_cityscapes_20200717_230920.log.json) | +| DeepLabV3+ | R-101-D8 | 512x1024 | 80000 | 6.46 | 2.60 | 80.46 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes-cc58bc8d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fp16/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes/deeplabv3plus_r101-d8_512x1024_80k_fp16_cityscapes_20200717_230920.log.json) | diff --git a/configs/gcnet/README.md b/configs/gcnet/README.md index b840d5bf9f8..9c7856a1d1e 100644 --- a/configs/gcnet/README.md +++ b/configs/gcnet/README.md @@ -18,31 +18,31 @@ ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| GCNet | R-50-D8 | 512x1024 | 40000 | 5.8 | 3.93 | 77.69 | 78.56 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436-4b0fd17b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436.log.json) | -| GCNet | R-101-D8 | 512x1024 | 40000 | 9.2 | 2.61 | 78.28 | 79.34 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436-5e62567f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436.log.json) | -| GCNet | R-50-D8 | 769x769 | 40000 | 6.5 | 1.67 | 78.12 | 80.09 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814-a26f4471.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814.log.json) | -| GCNet | R-101-D8 | 769x769 | 40000 | 10.5 | 1.13 | 78.95 | 80.71 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550-ca4f0a84.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550.log.json) | -| GCNet | R-50-D8 | 512x1024 | 80000 | - | - | 78.48 | 80.01 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450-ef8f069b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450.log.json) | -| GCNet | R-101-D8 | 512x1024 | 80000 | - | - | 79.03 | 79.84 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450-778ebf69.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450.log.json) | -| GCNet | R-50-D8 | 769x769 | 80000 | - | - | 78.68 | 80.66 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516-4839565b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516.log.json) | -| GCNet | R-101-D8 | 769x769 | 80000 | - | - | 79.18 | 80.71 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628-8e043423.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| GCNet | R-50-D8 | 512x1024 | 40000 | 5.8 | 3.93 | 77.69 | 78.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436-4b0fd17b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_40k_cityscapes/gcnet_r50-d8_512x1024_40k_cityscapes_20200618_074436.log.json) | +| GCNet | R-101-D8 | 512x1024 | 40000 | 9.2 | 2.61 | 78.28 | 79.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436-5e62567f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_40k_cityscapes/gcnet_r101-d8_512x1024_40k_cityscapes_20200618_074436.log.json) | +| GCNet | R-50-D8 | 769x769 | 40000 | 6.5 | 1.67 | 78.12 | 80.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814-a26f4471.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_40k_cityscapes/gcnet_r50-d8_769x769_40k_cityscapes_20200618_182814.log.json) | +| GCNet | R-101-D8 | 769x769 | 40000 | 10.5 | 1.13 | 78.95 | 80.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550-ca4f0a84.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_40k_cityscapes/gcnet_r101-d8_769x769_40k_cityscapes_20200619_092550.log.json) | +| GCNet | R-50-D8 | 512x1024 | 80000 | - | - | 78.48 | 80.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450-ef8f069b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x1024_80k_cityscapes/gcnet_r50-d8_512x1024_80k_cityscapes_20200618_074450.log.json) | +| GCNet | R-101-D8 | 512x1024 | 80000 | - | - | 79.03 | 79.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450-778ebf69.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x1024_80k_cityscapes/gcnet_r101-d8_512x1024_80k_cityscapes_20200618_074450.log.json) | +| GCNet | R-50-D8 | 769x769 | 80000 | - | - | 78.68 | 80.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516-4839565b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_769x769_80k_cityscapes/gcnet_r50-d8_769x769_80k_cityscapes_20200619_092516.log.json) | +| GCNet | R-101-D8 | 769x769 | 80000 | - | - | 79.18 | 80.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628-8e043423.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_769x769_80k_cityscapes/gcnet_r101-d8_769x769_80k_cityscapes_20200619_092628.log.json) | ### ADE20K -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| GCNet | R-50-D8 | 512x512 | 80000 | 8.5 | 23.38 | 41.47 | 42.85 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146-91a6da41.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146.log.json) | -| GCNet | R-101-D8 | 512x512 | 80000 | 12 | 15.20 | 42.82 | 44.54 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811-c3fcb6dd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811.log.json) | -| GCNet | R-50-D8 | 512x512 | 160000 | - | - | 42.37 | 43.52 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122-d95f3e1f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122.log.json) | -| GCNet | R-101-D8 | 512x512 | 160000 | - | - | 43.69 | 45.21 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406-615528d7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| GCNet | R-50-D8 | 512x512 | 80000 | 8.5 | 23.38 | 41.47 | 42.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146-91a6da41.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_80k_ade20k/gcnet_r50-d8_512x512_80k_ade20k_20200614_185146.log.json) | +| GCNet | R-101-D8 | 512x512 | 80000 | 12 | 15.20 | 42.82 | 44.54 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811-c3fcb6dd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_80k_ade20k/gcnet_r101-d8_512x512_80k_ade20k_20200615_020811.log.json) | +| GCNet | R-50-D8 | 512x512 | 160000 | - | - | 42.37 | 43.52 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122-d95f3e1f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_160k_ade20k/gcnet_r50-d8_512x512_160k_ade20k_20200615_224122.log.json) | +| GCNet | R-101-D8 | 512x512 | 160000 | - | - | 43.69 | 45.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406-615528d7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_160k_ade20k/gcnet_r101-d8_512x512_160k_ade20k_20200615_225406.log.json) | ### Pascal VOC 2012 + Aug -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| GCNet | R-50-D8 | 512x512 | 20000 | 5.8 | 23.35 | 76.42 | 77.51 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701-3cbfdab1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701.log.json) | -| GCNet | R-101-D8 | 512x512 | 20000 | 9.2 | 14.80 | 77.41 | 78.56 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713-6c720aa9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713.log.json) | -| GCNet | R-50-D8 | 512x512 | 40000 | - | - | 76.24 | 77.63 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105-9797336d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105.log.json) | -| GCNet | R-101-D8 | 512x512 | 40000 | - | - | 77.84 | 78.59 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806-1e38208d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| GCNet | R-50-D8 | 512x512 | 20000 | 5.8 | 23.35 | 76.42 | 77.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701-3cbfdab1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_20k_voc12aug/gcnet_r50-d8_512x512_20k_voc12aug_20200617_165701.log.json) | +| GCNet | R-101-D8 | 512x512 | 20000 | 9.2 | 14.80 | 77.41 | 78.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713-6c720aa9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_20k_voc12aug/gcnet_r101-d8_512x512_20k_voc12aug_20200617_165713.log.json) | +| GCNet | R-50-D8 | 512x512 | 40000 | - | - | 76.24 | 77.63 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105-9797336d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r50-d8_512x512_40k_voc12aug/gcnet_r50-d8_512x512_40k_voc12aug_20200613_195105.log.json) | +| GCNet | R-101-D8 | 512x512 | 40000 | - | - | 77.84 | 78.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet/gcnet_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806-1e38208d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/gcnet/gcnet_r101-d8_512x512_40k_voc12aug/gcnet_r101-d8_512x512_40k_voc12aug_20200613_185806.log.json) | diff --git a/configs/hrnet/README.md b/configs/hrnet/README.md index 2df6ec5b40d..1cf7597b752 100644 --- a/configs/hrnet/README.md +++ b/configs/hrnet/README.md @@ -17,46 +17,46 @@ ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|--------------------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| FCN | HRNetV2p-W18-Small | 512x1024 | 40000 | 1.7 | 23.74 | 73.86 | 75.91 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216-93db27d0.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216.log.json) | -| FCN | HRNetV2p-W18 | 512x1024 | 40000 | 2.9 | 12.97 | 77.19 | 78.92 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216-f196fb4e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216.log.json) | -| FCN | HRNetV2p-W48 | 512x1024 | 40000 | 6.2 | 6.42 | 78.48 | 79.69 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240-a989b146.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240.log.json) | -| FCN | HRNetV2p-W18-Small | 512x1024 | 80000 | - | - | 75.31 | 77.48 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700.log.json) | -| FCN | HRNetV2p-W18 | 512x1024 | 80000 | - | - | 78.65 | 80.35 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255.log.json) | -| FCN | HRNetV2p-W48 | 512x1024 | 80000 | - | - | 79.93 | 80.72 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606-58ea95d6.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606.log.json) | -| FCN | HRNetV2p-W18-Small | 512x1024 | 160000 | - | - | 76.31 | 78.31 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901.log.json) | -| FCN | HRNetV2p-W18 | 512x1024 | 160000 | - | - | 78.80 | 80.74 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822-221e4a4f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822.log.json) | -| FCN | HRNetV2p-W48 | 512x1024 | 160000 | - | - | 80.65 | 81.92 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | ------------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| FCN | HRNetV2p-W18-Small | 512x1024 | 40000 | 1.7 | 23.74 | 73.86 | 75.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216-93db27d0.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_40k_cityscapes/fcn_hr18s_512x1024_40k_cityscapes_20200601_014216.log.json) | +| FCN | HRNetV2p-W18 | 512x1024 | 40000 | 2.9 | 12.97 | 77.19 | 78.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216-f196fb4e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_40k_cityscapes/fcn_hr18_512x1024_40k_cityscapes_20200601_014216.log.json) | +| FCN | HRNetV2p-W48 | 512x1024 | 40000 | 6.2 | 6.42 | 78.48 | 79.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240-a989b146.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_40k_cityscapes/fcn_hr48_512x1024_40k_cityscapes_20200601_014240.log.json) | +| FCN | HRNetV2p-W18-Small | 512x1024 | 80000 | - | - | 75.31 | 77.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_80k_cityscapes/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700.log.json) | +| FCN | HRNetV2p-W18 | 512x1024 | 80000 | - | - | 78.65 | 80.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_80k_cityscapes/fcn_hr18_512x1024_80k_cityscapes_20200601_223255.log.json) | +| FCN | HRNetV2p-W48 | 512x1024 | 80000 | - | - | 79.93 | 80.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606-58ea95d6.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_80k_cityscapes/fcn_hr48_512x1024_80k_cityscapes_20200601_202606.log.json) | +| FCN | HRNetV2p-W18-Small | 512x1024 | 160000 | - | - | 76.31 | 78.31 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_512x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901-4a0797ea.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x1024_160k_cityscapes/fcn_hr18s_512x1024_160k_cityscapes_20200602_190901.log.json) | +| FCN | HRNetV2p-W18 | 512x1024 | 160000 | - | - | 78.80 | 80.74 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_512x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822-221e4a4f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x1024_160k_cityscapes/fcn_hr18_512x1024_160k_cityscapes_20200602_190822.log.json) | +| FCN | HRNetV2p-W48 | 512x1024 | 160000 | - | - | 80.65 | 81.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_512x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946-59b7973e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x1024_160k_cityscapes/fcn_hr48_512x1024_160k_cityscapes_20200602_190946.log.json) | ### ADE20K -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|--------------------|-----------|--------:|----------|----------------|------:|--------------:|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| FCN | HRNetV2p-W18-Small | 512x512 | 80000 | 3.8 | 38.66 | 31.38 | 32.45 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345-77fc814a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345.log.json) | -| FCN | HRNetV2p-W18 | 512x512 | 80000 | 4.9 | 22.57 | 35.51 | 36.80 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20200614_185145-66f20cb7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20200614_185145.log.json) | -| FCN | HRNetV2p-W48 | 512x512 | 80000 | 8.2 | 21.23 | 41.90 | 43.27 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946-7ba5258d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946.log.json) | -| FCN | HRNetV2p-W18-Small | 512x512 | 160000 | - | - | 33.00 | 34.55 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20200614_214413-870f65ac.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20200614_214413.log.json) | -| FCN | HRNetV2p-W18 | 512x512 | 160000 | - | - | 36.79 | 38.58 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426-ca961836.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426.log.json) | -| FCN | HRNetV2p-W48 | 512x512 | 160000 | - | - | 42.02 | 43.86 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | ------------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| FCN | HRNetV2p-W18-Small | 512x512 | 80000 | 3.8 | 38.66 | 31.38 | 32.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345-77fc814a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_80k_ade20k/fcn_hr18s_512x512_80k_ade20k_20200614_144345.log.json) | +| FCN | HRNetV2p-W18 | 512x512 | 80000 | 4.9 | 22.57 | 35.51 | 36.80 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20200614_185145-66f20cb7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_80k_ade20k/fcn_hr18_512x512_80k_ade20k_20200614_185145.log.json) | +| FCN | HRNetV2p-W48 | 512x512 | 80000 | 8.2 | 21.23 | 41.90 | 43.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946-7ba5258d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_80k_ade20k/fcn_hr48_512x512_80k_ade20k_20200614_193946.log.json) | +| FCN | HRNetV2p-W18-Small | 512x512 | 160000 | - | - | 33.00 | 34.55 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20200614_214413-870f65ac.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_160k_ade20k/fcn_hr18s_512x512_160k_ade20k_20200614_214413.log.json) | +| FCN | HRNetV2p-W18 | 512x512 | 160000 | - | - | 36.79 | 38.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426-ca961836.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_160k_ade20k/fcn_hr18_512x512_160k_ade20k_20200614_214426.log.json) | +| FCN | HRNetV2p-W48 | 512x512 | 160000 | - | - | 42.02 | 43.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407-a52fc02c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_160k_ade20k/fcn_hr48_512x512_160k_ade20k_20200614_214407.log.json) | ### Pascal VOC 2012 + Aug -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|--------------------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| FCN | HRNetV2p-W18-Small | 512x512 | 20000 | 1.8 | 43.36 | 65.20 | 68.55 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20200617_224503-56e36088.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20200617_224503.log.json) | -| FCN | HRNetV2p-W18 | 512x512 | 20000 | 2.9 | 23.48 | 72.30 | 74.71 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503-488d45f7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503.log.json) | -| FCN | HRNetV2p-W48 | 512x512 | 20000 | 6.2 | 22.05 | 75.87 | 78.58 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419-89de05cd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419.log.json) | -| FCN | HRNetV2p-W18-Small | 512x512 | 40000 | - | - | 66.61 | 70.00 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648-4f8d6e7f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648.log.json) | -| FCN | HRNetV2p-W18 | 512x512 | 40000 | - | - | 72.90 | 75.59 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401-1b4b76cd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401.log.json) | -| FCN | HRNetV2p-W48 | 512x512 | 40000 | - | - | 76.24 | 78.49 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111-1b0f18bc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | ------------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| FCN | HRNetV2p-W18-Small | 512x512 | 20000 | 1.8 | 43.36 | 65.20 | 68.55 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20200617_224503-56e36088.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_20k_voc12aug/fcn_hr18s_512x512_20k_voc12aug_20200617_224503.log.json) | +| FCN | HRNetV2p-W18 | 512x512 | 20000 | 2.9 | 23.48 | 72.30 | 74.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503-488d45f7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_20k_voc12aug/fcn_hr18_512x512_20k_voc12aug_20200617_224503.log.json) | +| FCN | HRNetV2p-W48 | 512x512 | 20000 | 6.2 | 22.05 | 75.87 | 78.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419-89de05cd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_20k_voc12aug/fcn_hr48_512x512_20k_voc12aug_20200617_224419.log.json) | +| FCN | HRNetV2p-W18-Small | 512x512 | 40000 | - | - | 66.61 | 70.00 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648-4f8d6e7f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18s_512x512_40k_voc12aug/fcn_hr18s_512x512_40k_voc12aug_20200614_000648.log.json) | +| FCN | HRNetV2p-W18 | 512x512 | 40000 | - | - | 72.90 | 75.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr18_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401-1b4b76cd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr18_512x512_40k_voc12aug/fcn_hr18_512x512_40k_voc12aug_20200613_224401.log.json) | +| FCN | HRNetV2p-W48 | 512x512 | 40000 | - | - | 76.24 | 78.49 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111-1b0f18bc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_512x512_40k_voc12aug/fcn_hr48_512x512_40k_voc12aug_20200613_222111.log.json) | ### Pascal Context -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|--------------------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| FCN | HRNetV2p-W48 | 480x480 | 40000 | 6.1 | 8.86 | 45.14 | 47.42 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context_20200911_164852-667d00b0.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context-20200911_164852.log.json) | -| FCN | HRNetV2p-W48 | 480x480 | 80000 | - | - | 45.84 | 47.84 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context_20200911_155322-847a6711.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context-20200911_155322.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | ------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| FCN | HRNetV2p-W48 | 480x480 | 40000 | 6.1 | 8.86 | 45.14 | 47.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_480x480_40k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context_20200911_164852-667d00b0.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_40k_pascal_context/fcn_hr48_480x480_40k_pascal_context-20200911_164852.log.json) | +| FCN | HRNetV2p-W48 | 480x480 | 80000 | - | - | 45.84 | 47.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet/fcn_hr48_480x480_80k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context_20200911_155322-847a6711.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/hrnet/fcn_hr48_480x480_80k_pascal_context/fcn_hr48_480x480_80k_pascal_context-20200911_155322.log.json) | ### Pascal Context 59 diff --git a/configs/mobilenet_v2/README.md b/configs/mobilenet_v2/README.md index e0e75e028db..2e6fa264e0a 100644 --- a/configs/mobilenet_v2/README.md +++ b/configs/mobilenet_v2/README.md @@ -18,18 +18,18 @@ ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|------------|----------|-----------|--------:|---------:|----------------|------:|---------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| FCN | M-V2-D8 | 512x1024 | 80000 | 3.4 | 14.2 | 61.54 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-d24c28c1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes-20200825_124817.log.json) | -| PSPNet | M-V2-D8 | 512x1024 | 80000 | 3.6 | 11.2 | 70.23 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-19e81d51.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes-20200825_124817.log.json) | -| DeepLabV3 | M-V2-D8 | 512x1024 | 80000 | 3.9 | 8.4 | 73.84 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-bef03590.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes-20200825_124836.log.json) | -| DeepLabV3+ | M-V2-D8 | 512x1024 | 80000 | 5.1 | 8.4 | 75.20 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-d256dd4b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes-20200825_124836.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ---------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ---------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| FCN | M-V2-D8 | 512x1024 | 80000 | 3.4 | 14.2 | 61.54 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-d24c28c1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes-20200825_124817.log.json) | +| PSPNet | M-V2-D8 | 512x1024 | 80000 | 3.6 | 11.2 | 70.23 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-19e81d51.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes-20200825_124817.log.json) | +| DeepLabV3 | M-V2-D8 | 512x1024 | 80000 | 3.9 | 8.4 | 73.84 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-bef03590.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes-20200825_124836.log.json) | +| DeepLabV3+ | M-V2-D8 | 512x1024 | 80000 | 5.1 | 8.4 | 75.20 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-d256dd4b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes-20200825_124836.log.json) | ### ADE20k -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|------------|----------|-----------|--------:|---------:|----------------|------:|---------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| FCN | M-V2-D8 | 512x512 | 160000 | 6.5 | 64.4 | 19.71 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k_20200825_214953-c40e1095.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k-20200825_214953.log.json) | -| PSPNet | M-V2-D8 | 512x512 | 160000 | 6.5 | 57.7 | 29.68 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k_20200825_214953-f5942f7a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k-20200825_214953.log.json) | -| DeepLabV3 | M-V2-D8 | 512x512 | 160000 | 6.8 | 39.9 | 34.08 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k_20200825_223255-63986343.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k-20200825_223255.log.json) | -| DeepLabV3+ | M-V2-D8 | 512x512 | 160000 | 8.2 | 43.1 | 34.02 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k_20200825_223255-465a01d4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k-20200825_223255.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ---------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| FCN | M-V2-D8 | 512x512 | 160000 | 6.5 | 64.4 | 19.71 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k_20200825_214953-c40e1095.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k-20200825_214953.log.json) | +| PSPNet | M-V2-D8 | 512x512 | 160000 | 6.5 | 57.7 | 29.68 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k_20200825_214953-f5942f7a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k-20200825_214953.log.json) | +| DeepLabV3 | M-V2-D8 | 512x512 | 160000 | 6.8 | 39.9 | 34.08 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k_20200825_223255-63986343.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k-20200825_223255.log.json) | +| DeepLabV3+ | M-V2-D8 | 512x512 | 160000 | 8.2 | 43.1 | 34.02 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k_20200825_223255-465a01d4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k-20200825_223255.log.json) | diff --git a/configs/mobilenet_v3/README.md b/configs/mobilenet_v3/README.md index 2bad2a731c6..3d987654469 100644 --- a/configs/mobilenet_v3/README.md +++ b/configs/mobilenet_v3/README.md @@ -20,9 +20,9 @@ ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|------------|----------|-----------|--------:|---------:|----------------|------:|---------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| LRASPP | M-V3-D8 | 512x1024 | 320000 | 8.9 | 15.22 | 69.54 | 70.89 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes/lraspp_m-v3-d8_512x1024_320k_cityscapes_20201224_220337-cfe8fb07.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes/lraspp_m-v3-d8_512x1024_320k_cityscapes-20201224_220337.log.json)| -| LRASPP | M-V3-D8 (scratch) | 512x1024 | 320000 | 8.9 | 14.77 | 67.87 | 69.78 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes_20201224_220337-9f29cd72.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes-20201224_220337.log.json)| -| LRASPP | M-V3s-D8 | 512x1024 | 320000 | 5.3 | 23.64 | 64.11 | 66.42 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes/lraspp_m-v3s-d8_512x1024_320k_cityscapes_20201224_223935-61565b34.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes/lraspp_m-v3s-d8_512x1024_320k_cityscapes-20201224_223935.log.json)| -| LRASPP | M-V3s-D8 (scratch) | 512x1024 | 320000 | 5.3 | 24.50 | 62.74 | 65.01 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes_20201224_223935-03daeabb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes-20201224_223935.log.json)| +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | ------------------ | --------- | ------: | -------: | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| LRASPP | M-V3-D8 | 512x1024 | 320000 | 8.9 | 15.22 | 69.54 | 70.89 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes/lraspp_m-v3-d8_512x1024_320k_cityscapes_20201224_220337-cfe8fb07.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_512x1024_320k_cityscapes/lraspp_m-v3-d8_512x1024_320k_cityscapes-20201224_220337.log.json) | +| LRASPP | M-V3-D8 (scratch) | 512x1024 | 320000 | 8.9 | 14.77 | 67.87 | 69.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes_20201224_220337-9f29cd72.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3-d8_scratch_512x1024_320k_cityscapes-20201224_220337.log.json) | +| LRASPP | M-V3s-D8 | 512x1024 | 320000 | 5.3 | 23.64 | 64.11 | 66.42 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes/lraspp_m-v3s-d8_512x1024_320k_cityscapes_20201224_223935-61565b34.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_512x1024_320k_cityscapes/lraspp_m-v3s-d8_512x1024_320k_cityscapes-20201224_223935.log.json) | +| LRASPP | M-V3s-D8 (scratch) | 512x1024 | 320000 | 5.3 | 24.50 | 62.74 | 65.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes_20201224_223935-03daeabb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v3/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes/lraspp_m-v3s-d8_scratch_512x1024_320k_cityscapes-20201224_223935.log.json) | diff --git a/configs/nonlocal_net/README.md b/configs/nonlocal_net/README.md index 76352e265a9..a52c5db8984 100644 --- a/configs/nonlocal_net/README.md +++ b/configs/nonlocal_net/README.md @@ -18,31 +18,31 @@ ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|----------|----------|-----------|--------:|----------|----------------|------:|---------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| NonLocal | R-50-D8 | 512x1024 | 40000 | 7.4 | 2.72 | 78.24 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748-c75e81e3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748.log.json) | -| NonLocal | R-101-D8 | 512x1024 | 40000 | 10.9 | 1.95 | 78.66 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748-d63729fa.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748.log.json) | -| NonLocal | R-50-D8 | 769x769 | 40000 | 8.9 | 1.52 | 78.33 | 79.92 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243-82ef6749.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243.log.json) | -| NonLocal | R-101-D8 | 769x769 | 40000 | 12.8 | 1.05 | 78.57 | 80.29 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348-8fe9a9dc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348.log.json) | -| NonLocal | R-50-D8 | 512x1024 | 80000 | - | - | 78.01 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518-d6839fae.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518.log.json) | -| NonLocal | R-101-D8 | 512x1024 | 80000 | - | - | 78.93 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411-32700183.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411.log.json) | -| NonLocal | R-50-D8 | 769x769 | 80000 | - | - | 79.05 | 80.68 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506-1f9792f6.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506.log.json) | -| NonLocal | R-101-D8 | 769x769 | 80000 | - | - | 79.40 | 80.85 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428-0e1fa4f9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| -------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------- | ----------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| NonLocal | R-50-D8 | 512x1024 | 40000 | 7.4 | 2.72 | 78.24 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748-c75e81e3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_40k_cityscapes/nonlocal_r50-d8_512x1024_40k_cityscapes_20200605_210748.log.json) | +| NonLocal | R-101-D8 | 512x1024 | 40000 | 10.9 | 1.95 | 78.66 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748-d63729fa.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes/nonlocal_r101-d8_512x1024_40k_cityscapes_20200605_210748.log.json) | +| NonLocal | R-50-D8 | 769x769 | 40000 | 8.9 | 1.52 | 78.33 | 79.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243-82ef6749.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_40k_cityscapes/nonlocal_r50-d8_769x769_40k_cityscapes_20200530_045243.log.json) | +| NonLocal | R-101-D8 | 769x769 | 40000 | 12.8 | 1.05 | 78.57 | 80.29 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348-8fe9a9dc.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes/nonlocal_r101-d8_769x769_40k_cityscapes_20200530_045348.log.json) | +| NonLocal | R-50-D8 | 512x1024 | 80000 | - | - | 78.01 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518-d6839fae.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x1024_80k_cityscapes/nonlocal_r50-d8_512x1024_80k_cityscapes_20200607_193518.log.json) | +| NonLocal | R-101-D8 | 512x1024 | 80000 | - | - | 78.93 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411-32700183.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x1024_80k_cityscapes/nonlocal_r101-d8_512x1024_80k_cityscapes_20200607_183411.log.json) | +| NonLocal | R-50-D8 | 769x769 | 80000 | - | - | 79.05 | 80.68 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506-1f9792f6.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_769x769_80k_cityscapes/nonlocal_r50-d8_769x769_80k_cityscapes_20200607_193506.log.json) | +| NonLocal | R-101-D8 | 769x769 | 80000 | - | - | 79.40 | 80.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428-0e1fa4f9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_769x769_80k_cityscapes/nonlocal_r101-d8_769x769_80k_cityscapes_20200607_183428.log.json) | ### ADE20K -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|----------|----------|-----------|--------:|----------|----------------|------:|--------------:|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| NonLocal | R-50-D8 | 512x512 | 80000 | 9.1 | 21.37 | 40.75 | 42.05 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801-5ae0aa33.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801.log.json) | -| NonLocal | R-101-D8 | 512x512 | 80000 | 12.6 | 13.97 | 42.90 | 44.27 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758-24105919.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758.log.json) | -| NonLocal | R-50-D8 | 512x512 | 160000 | - | - | 42.03 | 43.04 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410-baef45e3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410.log.json) | -| NonLocal | R-101-D8 | 512x512 | 160000 | - | - | 43.36 | 44.83 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20200616_003422-affd0f8d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20200616_003422.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| -------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| NonLocal | R-50-D8 | 512x512 | 80000 | 9.1 | 21.37 | 40.75 | 42.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801-5ae0aa33.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_80k_ade20k/nonlocal_r50-d8_512x512_80k_ade20k_20200615_015801.log.json) | +| NonLocal | R-101-D8 | 512x512 | 80000 | 12.6 | 13.97 | 42.90 | 44.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758-24105919.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_80k_ade20k/nonlocal_r101-d8_512x512_80k_ade20k_20200615_015758.log.json) | +| NonLocal | R-50-D8 | 512x512 | 160000 | - | - | 42.03 | 43.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410-baef45e3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_160k_ade20k/nonlocal_r50-d8_512x512_160k_ade20k_20200616_005410.log.json) | +| NonLocal | R-101-D8 | 512x512 | 160000 | - | - | 43.36 | 44.83 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20200616_003422-affd0f8d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_160k_ade20k/nonlocal_r101-d8_512x512_160k_ade20k_20200616_003422.log.json) | ### Pascal VOC 2012 + Aug -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|----------|----------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| NonLocal | R-50-D8 | 512x512 | 20000 | 6.4 | 21.21 | 76.20 | 77.12 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613-07f2a57c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613.log.json) | -| NonLocal | R-101-D8 | 512x512 | 20000 | 9.8 | 14.01 | 78.15 | 78.86 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615-948c68ab.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615.log.json) | -| NonLocal | R-50-D8 | 512x512 | 40000 | - | - | 76.65 | 77.47 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028-0139d4a9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028.log.json) | -| NonLocal | R-101-D8 | 512x512 | 40000 | - | - | 78.27 | 79.12 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028-7e5ff470.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| -------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| NonLocal | R-50-D8 | 512x512 | 20000 | 6.4 | 21.21 | 76.20 | 77.12 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613-07f2a57c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_20k_voc12aug/nonlocal_r50-d8_512x512_20k_voc12aug_20200617_222613.log.json) | +| NonLocal | R-101-D8 | 512x512 | 20000 | 9.8 | 14.01 | 78.15 | 78.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615-948c68ab.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_20k_voc12aug/nonlocal_r101-d8_512x512_20k_voc12aug_20200617_222615.log.json) | +| NonLocal | R-50-D8 | 512x512 | 40000 | - | - | 76.65 | 77.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028-0139d4a9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r50-d8_512x512_40k_voc12aug/nonlocal_r50-d8_512x512_40k_voc12aug_20200614_000028.log.json) | +| NonLocal | R-101-D8 | 512x512 | 40000 | - | - | 78.27 | 79.12 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028-7e5ff470.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/nonlocal_net/nonlocal_r101-d8_512x512_40k_voc12aug/nonlocal_r101-d8_512x512_40k_voc12aug_20200614_000028.log.json) | diff --git a/configs/ocrnet/README.md b/configs/ocrnet/README.md index 0a4c75c7083..5eea86283a0 100644 --- a/configs/ocrnet/README.md +++ b/configs/ocrnet/README.md @@ -26,44 +26,44 @@ #### HRNet backbone -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|--------------------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| OCRNet | HRNetV2p-W18-Small | 512x1024 | 40000 | 3.5 | 10.45 | 74.30 | 75.95 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes/ocrnet_hr18s_512x1024_40k_cityscapes_20200601_033304-fa2436c2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes/ocrnet_hr18s_512x1024_40k_cityscapes_20200601_033304.log.json) | -| OCRNet | HRNetV2p-W18 | 512x1024 | 40000 | 4.7 | 7.50 | 77.72 | 79.49 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320-401c5bdd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320.log.json) | -| OCRNet | HRNetV2p-W48 | 512x1024 | 40000 | 8 | 4.22 | 80.58 | 81.79 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336-55b32491.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336.log.json) | -| OCRNet | HRNetV2p-W18-Small | 512x1024 | 80000 | - | - | 77.16 | 78.66 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735-55979e63.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735.log.json) | -| OCRNet | HRNetV2p-W18 | 512x1024 | 80000 | - | - | 78.57 | 80.46 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521-c2e1dd4a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521.log.json) | -| OCRNet | HRNetV2p-W48 | 512x1024 | 80000 | - | - | 80.70 | 81.87 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752-9076bcdf.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752.log.json) | -| OCRNet | HRNetV2p-W18-Small | 512x1024 | 160000 | - | - | 78.45 | 79.97 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005-f4a7af28.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005.log.json) | -| OCRNet | HRNetV2p-W18 | 512x1024 | 160000 | - | - | 79.47 | 80.91 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001-b9172d0c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001.log.json) | -| OCRNet | HRNetV2p-W48 | 512x1024 | 160000 | - | - | 81.35 | 82.70 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037-dfbf1b0c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | ------------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| OCRNet | HRNetV2p-W18-Small | 512x1024 | 40000 | 3.5 | 10.45 | 74.30 | 75.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes/ocrnet_hr18s_512x1024_40k_cityscapes_20200601_033304-fa2436c2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes/ocrnet_hr18s_512x1024_40k_cityscapes_20200601_033304.log.json) | +| OCRNet | HRNetV2p-W18 | 512x1024 | 40000 | 4.7 | 7.50 | 77.72 | 79.49 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320-401c5bdd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_40k_cityscapes/ocrnet_hr18_512x1024_40k_cityscapes_20200601_033320.log.json) | +| OCRNet | HRNetV2p-W48 | 512x1024 | 40000 | 8 | 4.22 | 80.58 | 81.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336-55b32491.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_40k_cityscapes/ocrnet_hr48_512x1024_40k_cityscapes_20200601_033336.log.json) | +| OCRNet | HRNetV2p-W18-Small | 512x1024 | 80000 | - | - | 77.16 | 78.66 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735-55979e63.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_80k_cityscapes/ocrnet_hr18s_512x1024_80k_cityscapes_20200601_222735.log.json) | +| OCRNet | HRNetV2p-W18 | 512x1024 | 80000 | - | - | 78.57 | 80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521-c2e1dd4a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_80k_cityscapes/ocrnet_hr18_512x1024_80k_cityscapes_20200614_230521.log.json) | +| OCRNet | HRNetV2p-W48 | 512x1024 | 80000 | - | - | 80.70 | 81.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752-9076bcdf.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_80k_cityscapes/ocrnet_hr48_512x1024_80k_cityscapes_20200601_222752.log.json) | +| OCRNet | HRNetV2p-W18-Small | 512x1024 | 160000 | - | - | 78.45 | 79.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005-f4a7af28.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x1024_160k_cityscapes/ocrnet_hr18s_512x1024_160k_cityscapes_20200602_191005.log.json) | +| OCRNet | HRNetV2p-W18 | 512x1024 | 160000 | - | - | 79.47 | 80.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001-b9172d0c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x1024_160k_cityscapes/ocrnet_hr18_512x1024_160k_cityscapes_20200602_191001.log.json) | +| OCRNet | HRNetV2p-W48 | 512x1024 | 160000 | - | - | 81.35 | 82.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037-dfbf1b0c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x1024_160k_cityscapes/ocrnet_hr48_512x1024_160k_cityscapes_20200602_191037.log.json) | #### ResNet backbone -| Method | Backbone | Crop Size | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|--------------------|-----------|--------|----------|-----------|----------------|------|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| OCRNet | R-101-D8 | 512x1024 | 8 | 40000 | - | - | 80.09 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes-02ac0f13.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes_20200717_110721.log.json) | -| OCRNet | R-101-D8 | 512x1024 | 16 | 40000 | 8.8 | 3.02 | 80.30 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes-db500f80.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes_20200723_193726.log.json) | -| OCRNet | R-101-D8 | 512x1024 | 16 | 80000 | 8.8 | 3.02 | 80.81 | - | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes/ocrnet_r101-d8_512x1024_80k_b16_cityscapes-78688424.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes/ocrnet_r101-d8_512x1024_80k_b16_cityscapes_20200723_192421.log.json) | +| Method | Backbone | Crop Size | Batch Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ---------- | ------- | -------- | -------------- | ----- | ------------: | ------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| OCRNet | R-101-D8 | 512x1024 | 8 | 40000 | - | - | 80.09 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes-02ac0f13.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b8_cityscapes/ocrnet_r101-d8_512x1024_40k_b8_cityscapes_20200717_110721.log.json) | +| OCRNet | R-101-D8 | 512x1024 | 16 | 40000 | 8.8 | 3.02 | 80.30 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes-db500f80.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_40k_b16_cityscapes/ocrnet_r101-d8_512x1024_40k_b16_cityscapes_20200723_193726.log.json) | +| OCRNet | R-101-D8 | 512x1024 | 16 | 80000 | 8.8 | 3.02 | 80.81 | - | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes/ocrnet_r101-d8_512x1024_80k_b16_cityscapes-78688424.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_r101-d8_512x1024_80k_b16_cityscapes/ocrnet_r101-d8_512x1024_80k_b16_cityscapes_20200723_192421.log.json) | ### ADE20K -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|--------------------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| OCRNet | HRNetV2p-W18-Small | 512x512 | 80000 | 6.7 | 28.98 | 35.06 | 35.80 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600-e80b62af.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600.log.json) | -| OCRNet | HRNetV2p-W18 | 512x512 | 80000 | 7.9 | 18.93 | 37.79 | 39.16 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157-d173d83b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157.log.json) | -| OCRNet | HRNetV2p-W48 | 512x512 | 80000 | 11.2 | 16.99 | 43.00 | 44.30 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518-d168c2d1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518.log.json) | -| OCRNet | HRNetV2p-W18-Small | 512x512 | 160000 | - | - | 37.19 | 38.40 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505-8e913058.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505.log.json) | -| OCRNet | HRNetV2p-W18 | 512x512 | 160000 | - | - | 39.32 | 40.80 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940-d8fcd9d1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940.log.json) | -| OCRNet | HRNetV2p-W48 | 512x512 | 160000 | - | - | 43.25 | 44.88 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705-a073726d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | ------------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| OCRNet | HRNetV2p-W18-Small | 512x512 | 80000 | 6.7 | 28.98 | 35.06 | 35.80 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr18s_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600-e80b62af.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_80k_ade20k/ocrnet_hr18s_512x512_80k_ade20k_20200615_055600.log.json) | +| OCRNet | HRNetV2p-W18 | 512x512 | 80000 | 7.9 | 18.93 | 37.79 | 39.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr18_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157-d173d83b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_80k_ade20k/ocrnet_hr18_512x512_80k_ade20k_20200615_053157.log.json) | +| OCRNet | HRNetV2p-W48 | 512x512 | 80000 | 11.2 | 16.99 | 43.00 | 44.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr48_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518-d168c2d1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_80k_ade20k/ocrnet_hr48_512x512_80k_ade20k_20200615_021518.log.json) | +| OCRNet | HRNetV2p-W18-Small | 512x512 | 160000 | - | - | 37.19 | 38.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr18s_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505-8e913058.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_160k_ade20k/ocrnet_hr18s_512x512_160k_ade20k_20200615_184505.log.json) | +| OCRNet | HRNetV2p-W18 | 512x512 | 160000 | - | - | 39.32 | 40.80 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr18_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940-d8fcd9d1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_160k_ade20k/ocrnet_hr18_512x512_160k_ade20k_20200615_200940.log.json) | +| OCRNet | HRNetV2p-W48 | 512x512 | 160000 | - | - | 43.25 | 44.88 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr48_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705-a073726d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_160k_ade20k/ocrnet_hr48_512x512_160k_ade20k_20200615_184705.log.json) | ### Pascal VOC 2012 + Aug -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|--------------------|-----------|--------:|----------|----------------|------:|--------------:|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| OCRNet | HRNetV2p-W18-Small | 512x512 | 20000 | 3.5 | 31.55 | 71.70 | 73.84 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913-02b04fcb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913.log.json) | -| OCRNet | HRNetV2p-W18 | 512x512 | 20000 | 4.7 | 19.91 | 74.75 | 77.11 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932-8954cbb7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932.log.json) | -| OCRNet | HRNetV2p-W48 | 512x512 | 20000 | 8.1 | 17.83 | 77.72 | 79.87 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932-9e82080a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932.log.json) | -| OCRNet | HRNetV2p-W18-Small | 512x512 | 40000 | - | - | 72.76 | 74.60 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025-42b587ac.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025.log.json) | -| OCRNet | HRNetV2p-W18 | 512x512 | 40000 | - | - | 74.98 | 77.40 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958-714302be.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958.log.json) | -| OCRNet | HRNetV2p-W48 | 512x512 | 40000 | - | - | 77.14 | 79.71 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958-255bc5ce.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | ------------------ | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| OCRNet | HRNetV2p-W18-Small | 512x512 | 20000 | 3.5 | 31.55 | 71.70 | 73.84 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913-02b04fcb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_20k_voc12aug/ocrnet_hr18s_512x512_20k_voc12aug_20200617_233913.log.json) | +| OCRNet | HRNetV2p-W18 | 512x512 | 20000 | 4.7 | 19.91 | 74.75 | 77.11 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr18_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932-8954cbb7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_20k_voc12aug/ocrnet_hr18_512x512_20k_voc12aug_20200617_233932.log.json) | +| OCRNet | HRNetV2p-W48 | 512x512 | 20000 | 8.1 | 17.83 | 77.72 | 79.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr48_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932-9e82080a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_20k_voc12aug/ocrnet_hr48_512x512_20k_voc12aug_20200617_233932.log.json) | +| OCRNet | HRNetV2p-W18-Small | 512x512 | 40000 | - | - | 72.76 | 74.60 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025-42b587ac.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18s_512x512_40k_voc12aug/ocrnet_hr18s_512x512_40k_voc12aug_20200614_002025.log.json) | +| OCRNet | HRNetV2p-W18 | 512x512 | 40000 | - | - | 74.98 | 77.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr18_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958-714302be.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr18_512x512_40k_voc12aug/ocrnet_hr18_512x512_40k_voc12aug_20200614_015958.log.json) | +| OCRNet | HRNetV2p-W48 | 512x512 | 40000 | - | - | 77.14 | 79.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet/ocrnet_hr48_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958-255bc5ce.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/ocrnet/ocrnet_hr48_512x512_40k_voc12aug/ocrnet_hr48_512x512_40k_voc12aug_20200614_015958.log.json) | diff --git a/configs/point_rend/README.md b/configs/point_rend/README.md index 0dea3e31f8c..af429665e85 100644 --- a/configs/point_rend/README.md +++ b/configs/point_rend/README.md @@ -19,14 +19,14 @@ ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|-----------|----------|-----------|--------:|---------:|----------------|------:|---------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| PointRend | R-50 | 512x1024 | 80000 | 3.1 | 8.48 | 76.47 | 78.13 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes_20200711_015821-bb1ff523.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes-20200715_214714.log.json) | -| PointRend | R-101 | 512x1024 | 80000 | 4.2 | 7.00 | 78.30 | 79.97 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes_20200711_170850-d0ca84be.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes-20200715_214824.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| --------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| PointRend | R-50 | 512x1024 | 80000 | 3.1 | 8.48 | 76.47 | 78.13 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/point_rend/pointrend_r50_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes_20200711_015821-bb1ff523.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x1024_80k_cityscapes/pointrend_r50_512x1024_80k_cityscapes-20200715_214714.log.json) | +| PointRend | R-101 | 512x1024 | 80000 | 4.2 | 7.00 | 78.30 | 79.97 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/point_rend/pointrend_r101_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes_20200711_170850-d0ca84be.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x1024_80k_cityscapes/pointrend_r101_512x1024_80k_cityscapes-20200715_214824.log.json) | ### ADE20K -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|-----------|----------|-----------|--------:|---------:|----------------|------:|---------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| PointRend | R-50 | 512x512 | 160000 | 5.1 | 17.31 | 37.64 | 39.17 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k_20200807_232644-ac3febf2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k-20200807_232644.log.json) | -| PointRend | R-101 | 512x512 | 160000 | 6.1 | 15.50 | 40.02 | 41.60 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k_20200808_030852-8834902a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k-20200808_030852.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| --------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| PointRend | R-50 | 512x512 | 160000 | 5.1 | 17.31 | 37.64 | 39.17 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/point_rend/pointrend_r50_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k_20200807_232644-ac3febf2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r50_512x512_160k_ade20k/pointrend_r50_512x512_160k_ade20k-20200807_232644.log.json) | +| PointRend | R-101 | 512x512 | 160000 | 6.1 | 15.50 | 40.02 | 41.60 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/point_rend/pointrend_r101_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k_20200808_030852-8834902a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/point_rend/pointrend_r101_512x512_160k_ade20k/pointrend_r101_512x512_160k_ade20k-20200808_030852.log.json) | diff --git a/configs/psanet/README.md b/configs/psanet/README.md index fcb24103b8e..16c2cf95961 100644 --- a/configs/psanet/README.md +++ b/configs/psanet/README.md @@ -18,31 +18,31 @@ ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| PSANet | R-50-D8 | 512x1024 | 40000 | 7 | 3.17 | 77.63 | 79.04 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117-99fac37c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117.log.json) | -| PSANet | R-101-D8 | 512x1024 | 40000 | 10.5 | 2.20 | 79.14 | 80.19 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418-27b9cfa7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418.log.json) | -| PSANet | R-50-D8 | 769x769 | 40000 | 7.9 | 1.40 | 77.99 | 79.64 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717-d5365506.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717.log.json) | -| PSANet | R-101-D8 | 769x769 | 40000 | 11.9 | 0.98 | 78.43 | 80.26 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107-997da1e6.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107.log.json) | -| PSANet | R-50-D8 | 512x1024 | 80000 | - | - | 77.24 | 78.69 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842-ab60a24f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842.log.json) | -| PSANet | R-101-D8 | 512x1024 | 80000 | - | - | 79.31 | 80.53 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823-0f73a169.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823.log.json) | -| PSANet | R-50-D8 | 769x769 | 80000 | - | - | 79.31 | 80.91 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134-fe42f49e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134.log.json) | -| PSANet | R-101-D8 | 769x769 | 80000 | - | - | 79.69 | 80.89 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550-7665827b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| PSANet | R-50-D8 | 512x1024 | 40000 | 7 | 3.17 | 77.63 | 79.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117-99fac37c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_40k_cityscapes/psanet_r50-d8_512x1024_40k_cityscapes_20200606_103117.log.json) | +| PSANet | R-101-D8 | 512x1024 | 40000 | 10.5 | 2.20 | 79.14 | 80.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418-27b9cfa7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_40k_cityscapes/psanet_r101-d8_512x1024_40k_cityscapes_20200606_001418.log.json) | +| PSANet | R-50-D8 | 769x769 | 40000 | 7.9 | 1.40 | 77.99 | 79.64 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717-d5365506.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_40k_cityscapes/psanet_r50-d8_769x769_40k_cityscapes_20200530_033717.log.json) | +| PSANet | R-101-D8 | 769x769 | 40000 | 11.9 | 0.98 | 78.43 | 80.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107-997da1e6.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_40k_cityscapes/psanet_r101-d8_769x769_40k_cityscapes_20200530_035107.log.json) | +| PSANet | R-50-D8 | 512x1024 | 80000 | - | - | 77.24 | 78.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842-ab60a24f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x1024_80k_cityscapes/psanet_r50-d8_512x1024_80k_cityscapes_20200606_161842.log.json) | +| PSANet | R-101-D8 | 512x1024 | 80000 | - | - | 79.31 | 80.53 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823-0f73a169.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x1024_80k_cityscapes/psanet_r101-d8_512x1024_80k_cityscapes_20200606_161823.log.json) | +| PSANet | R-50-D8 | 769x769 | 80000 | - | - | 79.31 | 80.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134-fe42f49e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_769x769_80k_cityscapes/psanet_r50-d8_769x769_80k_cityscapes_20200606_225134.log.json) | +| PSANet | R-101-D8 | 769x769 | 80000 | - | - | 79.69 | 80.89 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550-7665827b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_769x769_80k_cityscapes/psanet_r101-d8_769x769_80k_cityscapes_20200606_214550.log.json) | ### ADE20K -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| PSANet | R-50-D8 | 512x512 | 80000 | 9 | 18.91 | 41.14 | 41.91 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141-835e4b97.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141.log.json) | -| PSANet | R-101-D8 | 512x512 | 80000 | 12.5 | 13.13 | 43.80 | 44.75 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117-1fab60d4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117.log.json) | -| PSANet | R-50-D8 | 512x512 | 160000 | - | - | 41.67 | 42.95 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258-148077dd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258.log.json) | -| PSANet | R-101-D8 | 512x512 | 160000 | - | - | 43.74 | 45.38 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537-dbfa564c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| PSANet | R-50-D8 | 512x512 | 80000 | 9 | 18.91 | 41.14 | 41.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141-835e4b97.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_80k_ade20k/psanet_r50-d8_512x512_80k_ade20k_20200614_144141.log.json) | +| PSANet | R-101-D8 | 512x512 | 80000 | 12.5 | 13.13 | 43.80 | 44.75 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117-1fab60d4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_80k_ade20k/psanet_r101-d8_512x512_80k_ade20k_20200614_185117.log.json) | +| PSANet | R-50-D8 | 512x512 | 160000 | - | - | 41.67 | 42.95 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258-148077dd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_160k_ade20k/psanet_r50-d8_512x512_160k_ade20k_20200615_161258.log.json) | +| PSANet | R-101-D8 | 512x512 | 160000 | - | - | 43.74 | 45.38 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537-dbfa564c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_160k_ade20k/psanet_r101-d8_512x512_160k_ade20k_20200615_161537.log.json) | ### Pascal VOC 2012 + Aug -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| PSANet | R-50-D8 | 512x512 | 20000 | 6.9 | 18.24 | 76.39 | 77.34 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413-2f1bbaa1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413.log.json) | -| PSANet | R-101-D8 | 512x512 | 20000 | 10.4 | 12.63 | 77.91 | 79.30 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624-946fef11.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624.log.json) | -| PSANet | R-50-D8 | 512x512 | 40000 | - | - | 76.30 | 77.35 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946-f596afb5.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946.log.json) | -| PSANet | R-101-D8 | 512x512 | 40000 | - | - | 77.73 | 79.05 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946-1f560f9e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| PSANet | R-50-D8 | 512x512 | 20000 | 6.9 | 18.24 | 76.39 | 77.34 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413-2f1bbaa1.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_20k_voc12aug/psanet_r50-d8_512x512_20k_voc12aug_20200617_102413.log.json) | +| PSANet | R-101-D8 | 512x512 | 20000 | 10.4 | 12.63 | 77.91 | 79.30 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624-946fef11.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_20k_voc12aug/psanet_r101-d8_512x512_20k_voc12aug_20200617_110624.log.json) | +| PSANet | R-50-D8 | 512x512 | 40000 | - | - | 76.30 | 77.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946-f596afb5.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r50-d8_512x512_40k_voc12aug/psanet_r50-d8_512x512_40k_voc12aug_20200613_161946.log.json) | +| PSANet | R-101-D8 | 512x512 | 40000 | - | - | 77.73 | 79.05 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet/psanet_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946-1f560f9e.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/psanet/psanet_r101-d8_512x512_40k_voc12aug/psanet_r101-d8_512x512_40k_voc12aug_20200613_161946.log.json) | diff --git a/configs/pspnet/README.md b/configs/pspnet/README.md index 0a72730b551..34ca237c3ef 100644 --- a/configs/pspnet/README.md +++ b/configs/pspnet/README.md @@ -17,49 +17,49 @@ ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| PSPNet | R-50-D8 | 512x1024 | 40000 | 6.1 | 4.07 | 77.85 | 79.18 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338.log.json) | -| PSPNet | R-101-D8 | 512x1024 | 40000 | 9.6 | 2.68 | 78.34 | 79.74 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751.log.json) | -| PSPNet | R-50-D8 | 769x769 | 40000 | 6.9 | 1.76 | 78.26 | 79.88 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725-86638686.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725.log.json) | -| PSPNet | R-101-D8 | 769x769 | 40000 | 10.9 | 1.15 | 79.08 | 80.28 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753-61c6f5be.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753.log.json) | -| PSPNet | R-18-D8 | 512x1024 | 80000 | 1.7 | 15.71 | 74.87 | 76.04 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes_20201225_021458-09ffa746.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes-20201225_021458.log.json) | -| PSPNet | R-50-D8 | 512x1024 | 80000 | - | - | 78.55 | 79.79 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131-2376f12b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131.log.json) | -| PSPNet | R-101-D8 | 512x1024 | 80000 | - | - | 79.76 | 81.01 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211.log.json) | -| PSPNet | R-18-D8 | 769x769 | 80000 | 1.9 | 6.20 | 75.90 | 77.86 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes_20201225_021458-3deefc62.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes-20201225_021458.log.json) | -| PSPNet | R-50-D8 | 769x769 | 80000 | - | - | 79.59 | 80.69 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121-5ccf03dd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121.log.json) | -| PSPNet | R-101-D8 | 769x769 | 80000 | - | - | 79.77 | 81.06 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055-dba412fa.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055.log.json) | -| PSPNet | R-18b-D8 | 512x1024 | 80000 | 1.5 | 16.28 | 74.23 | 75.79 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes_20201226_063116-26928a60.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes-20201226_063116.log.json) | -| PSPNet | R-50b-D8 | 512x1024 | 80000 | 6.0 | 4.30 | 78.22 | 79.46 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes_20201225_094315-6344287a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes-20201225_094315.log.json) | -| PSPNet | R-101b-D8| 512x1024 | 80000 | 9.5 | 2.76 | 79.69 | 80.79 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes-20201226_170012.log.json) | -| PSPNet | R-18b-D8 | 769x769 | 80000 | 1.7 | 6.41 | 74.92 | 76.90 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes_20201226_080942-bf98d186.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes-20201226_080942.log.json) | -| PSPNet | R-50b-D8 | 769x769 | 80000 | 6.8 | 1.88 | 78.50 | 79.96 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes_20201225_094316-4c643cf6.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes-20201225_094316.log.json) | -| PSPNet | R-101b-D8| 769x769 | 80000 | 10.8 | 1.17 | 78.87 | 80.04 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes_20201226_171823-f0e7c293.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes-20201226_171823.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | --------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| PSPNet | R-50-D8 | 512x1024 | 40000 | 6.1 | 4.07 | 77.85 | 79.18 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338.log.json) | +| PSPNet | R-101-D8 | 512x1024 | 40000 | 9.6 | 2.68 | 78.34 | 79.74 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751-467e7cf4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_40k_cityscapes/pspnet_r101-d8_512x1024_40k_cityscapes_20200604_232751.log.json) | +| PSPNet | R-50-D8 | 769x769 | 40000 | 6.9 | 1.76 | 78.26 | 79.88 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725-86638686.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_40k_cityscapes/pspnet_r50-d8_769x769_40k_cityscapes_20200606_112725.log.json) | +| PSPNet | R-101-D8 | 769x769 | 40000 | 10.9 | 1.15 | 79.08 | 80.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753-61c6f5be.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_40k_cityscapes/pspnet_r101-d8_769x769_40k_cityscapes_20200606_112753.log.json) | +| PSPNet | R-18-D8 | 512x1024 | 80000 | 1.7 | 15.71 | 74.87 | 76.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes_20201225_021458-09ffa746.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes/pspnet_r18-d8_512x1024_80k_cityscapes-20201225_021458.log.json) | +| PSPNet | R-50-D8 | 512x1024 | 80000 | - | - | 78.55 | 79.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131-2376f12b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x1024_80k_cityscapes/pspnet_r50-d8_512x1024_80k_cityscapes_20200606_112131.log.json) | +| PSPNet | R-101-D8 | 512x1024 | 80000 | - | - | 79.76 | 81.01 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211.log.json) | +| PSPNet | R-18-D8 | 769x769 | 80000 | 1.9 | 6.20 | 75.90 | 77.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r18-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes_20201225_021458-3deefc62.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18-d8_769x769_80k_cityscapes/pspnet_r18-d8_769x769_80k_cityscapes-20201225_021458.log.json) | +| PSPNet | R-50-D8 | 769x769 | 80000 | - | - | 79.59 | 80.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121-5ccf03dd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_769x769_80k_cityscapes/pspnet_r50-d8_769x769_80k_cityscapes_20200606_210121.log.json) | +| PSPNet | R-101-D8 | 769x769 | 80000 | - | - | 79.77 | 81.06 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055-dba412fa.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_769x769_80k_cityscapes/pspnet_r101-d8_769x769_80k_cityscapes_20200606_225055.log.json) | +| PSPNet | R-18b-D8 | 512x1024 | 80000 | 1.5 | 16.28 | 74.23 | 75.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes_20201226_063116-26928a60.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes/pspnet_r18b-d8_512x1024_80k_cityscapes-20201226_063116.log.json) | +| PSPNet | R-50b-D8 | 512x1024 | 80000 | 6.0 | 4.30 | 78.22 | 79.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes_20201225_094315-6344287a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes/pspnet_r50b-d8_512x1024_80k_cityscapes-20201225_094315.log.json) | +| PSPNet | R-101b-D8 | 512x1024 | 80000 | 9.5 | 2.76 | 79.69 | 80.79 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes_20201226_170012-3a4d38ab.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes/pspnet_r101b-d8_512x1024_80k_cityscapes-20201226_170012.log.json) | +| PSPNet | R-18b-D8 | 769x769 | 80000 | 1.7 | 6.41 | 74.92 | 76.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes_20201226_080942-bf98d186.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes/pspnet_r18b-d8_769x769_80k_cityscapes-20201226_080942.log.json) | +| PSPNet | R-50b-D8 | 769x769 | 80000 | 6.8 | 1.88 | 78.50 | 79.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes_20201225_094316-4c643cf6.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes/pspnet_r50b-d8_769x769_80k_cityscapes-20201225_094316.log.json) | +| PSPNet | R-101b-D8 | 769x769 | 80000 | 10.8 | 1.17 | 78.87 | 80.04 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes_20201226_171823-f0e7c293.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes/pspnet_r101b-d8_769x769_80k_cityscapes-20201226_171823.log.json) | ### ADE20K -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| PSPNet | R-50-D8 | 512x512 | 80000 | 8.5 | 23.53 | 41.13 | 41.94 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128-15a8b914.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128.log.json) | -| PSPNet | R-101-D8 | 512x512 | 80000 | 12 | 15.30 | 43.57 | 44.35 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423-b6e782f0.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423.log.json) | -| PSPNet | R-50-D8 | 512x512 | 160000 | - | - | 42.48 | 43.44 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358-1890b0bd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358.log.json) | -| PSPNet | R-101-D8 | 512x512 | 160000 | - | - | 44.39 | 45.35 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650-967c316f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| PSPNet | R-50-D8 | 512x512 | 80000 | 8.5 | 23.53 | 41.13 | 41.94 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128-15a8b914.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_80k_ade20k/pspnet_r50-d8_512x512_80k_ade20k_20200615_014128.log.json) | +| PSPNet | R-101-D8 | 512x512 | 80000 | 12 | 15.30 | 43.57 | 44.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423-b6e782f0.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_80k_ade20k/pspnet_r101-d8_512x512_80k_ade20k_20200614_031423.log.json) | +| PSPNet | R-50-D8 | 512x512 | 160000 | - | - | 42.48 | 43.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358-1890b0bd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_160k_ade20k/pspnet_r50-d8_512x512_160k_ade20k_20200615_184358.log.json) | +| PSPNet | R-101-D8 | 512x512 | 160000 | - | - | 44.39 | 45.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650-967c316f.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_160k_ade20k/pspnet_r101-d8_512x512_160k_ade20k_20200615_100650.log.json) | ### Pascal VOC 2012 + Aug -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| PSPNet | R-50-D8 | 512x512 | 20000 | 6.1 | 23.59 | 76.78 | 77.61 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958-ed5dfbd9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958.log.json) | -| PSPNet | R-101-D8 | 512x512 | 20000 | 9.6 | 15.02 | 78.47 | 79.25 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003-4aef3c9a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003.log.json) | -| PSPNet | R-50-D8 | 512x512 | 40000 | - | - | 77.29 | 78.48 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222-ae9c1b8c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222.log.json) | -| PSPNet | R-101-D8 | 512x512 | 40000 | - | - | 78.52 | 79.57 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222-bc933b18.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| PSPNet | R-50-D8 | 512x512 | 20000 | 6.1 | 23.59 | 76.78 | 77.61 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958-ed5dfbd9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_20k_voc12aug/pspnet_r50-d8_512x512_20k_voc12aug_20200617_101958.log.json) | +| PSPNet | R-101-D8 | 512x512 | 20000 | 9.6 | 15.02 | 78.47 | 79.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003-4aef3c9a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_20k_voc12aug/pspnet_r101-d8_512x512_20k_voc12aug_20200617_102003.log.json) | +| PSPNet | R-50-D8 | 512x512 | 40000 | - | - | 77.29 | 78.48 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r50-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222-ae9c1b8c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r50-d8_512x512_40k_voc12aug/pspnet_r50-d8_512x512_40k_voc12aug_20200613_161222.log.json) | +| PSPNet | R-101-D8 | 512x512 | 40000 | - | - | 78.52 | 79.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222-bc933b18.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x512_40k_voc12aug/pspnet_r101-d8_512x512_40k_voc12aug_20200613_161222.log.json) | ### Pascal Context -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| PSPNet | R-101-D8 | 480x480 | 40000 | 8.8 | 9.68 | 46.60 | 47.78 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context_20200911_211210-bf0f5d7c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context-20200911_211210.log.json) | -| PSPNet | R-101-D8 | 480x480 | 80000 | - | - | 46.03 | 47.15 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context_20200911_190530-c86d6233.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context-20200911_190530.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| PSPNet | R-101-D8 | 480x480 | 40000 | 8.8 | 9.68 | 46.60 | 47.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_480x480_40k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context_20200911_211210-bf0f5d7c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_40k_pascal_context/pspnet_r101-d8_480x480_40k_pascal_context-20200911_211210.log.json) | +| PSPNet | R-101-D8 | 480x480 | 80000 | - | - | 46.03 | 47.15 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context_20200911_190530-c86d6233.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_480x480_80k_pascal_context/pspnet_r101-d8_480x480_80k_pascal_context-20200911_190530.log.json) | ### Pascal Context 59 diff --git a/configs/resnest/README.md b/configs/resnest/README.md index 31bac01ec9f..c0980d93731 100644 --- a/configs/resnest/README.md +++ b/configs/resnest/README.md @@ -17,18 +17,18 @@ year={2020} ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|------------|----------|-----------|--------:|---------:|----------------|------:|---------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| FCN | S-101-D8 | 512x1024 | 80000 | 11.4 | 2.39 | 77.56 | 78.98 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes_20200807_140631-f8d155b3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes-20200807_140631.log.json) | -| PSPNet | S-101-D8 | 512x1024 | 80000 | 11.8 | 2.52 | 78.57 | 79.19 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes_20200807_140631-c75f3b99.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes-20200807_140631.log.json) | -| DeepLabV3 | S-101-D8 | 512x1024 | 80000 | 11.9 | 1.88 | 79.67 | 80.51 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes_20200807_144429-b73c4270.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes-20200807_144429.log.json) | -| DeepLabV3+ | S-101-D8 | 512x1024 | 80000 | 13.2 | 2.36 | 79.62 | 80.27 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes_20200807_144429-1239eb43.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes-20200807_144429.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ---------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ----------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| FCN | S-101-D8 | 512x1024 | 80000 | 11.4 | 2.39 | 77.56 | 78.98 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/resnest/fcn_s101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes_20200807_140631-f8d155b3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes-20200807_140631.log.json) | +| PSPNet | S-101-D8 | 512x1024 | 80000 | 11.8 | 2.52 | 78.57 | 79.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes_20200807_140631-c75f3b99.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes-20200807_140631.log.json) | +| DeepLabV3 | S-101-D8 | 512x1024 | 80000 | 11.9 | 1.88 | 79.67 | 80.51 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes_20200807_144429-b73c4270.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes-20200807_144429.log.json) | +| DeepLabV3+ | S-101-D8 | 512x1024 | 80000 | 13.2 | 2.36 | 79.62 | 80.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes_20200807_144429-1239eb43.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes-20200807_144429.log.json) | ### ADE20k -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|------------|----------|-----------|--------:|---------:|----------------|------:|---------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| FCN | S-101-D8 | 512x512 | 160000 | 14.2 | 12.86 | 45.62 | 46.16 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k_20200807_145416-d3160329.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k-20200807_145416.log.json) | -| PSPNet | S-101-D8 | 512x512 | 160000 | 14.2 | 13.02 | 45.44 | 46.28 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k_20200807_145416-a6daa92a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k-20200807_145416.log.json) | -| DeepLabV3 | S-101-D8 | 512x512 | 160000 | 14.6 | 9.28 | 45.71 | 46.59 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k_20200807_144503-17ecabe5.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k-20200807_144503.log.json) | -| DeepLabV3+ | S-101-D8 | 512x512 | 160000 | 16.2 | 11.96 | 46.47 | 47.27 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k_20200807_144503-27b26226.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k-20200807_144503.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ---------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| FCN | S-101-D8 | 512x512 | 160000 | 14.2 | 12.86 | 45.62 | 46.16 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/resnest/fcn_s101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k_20200807_145416-d3160329.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k-20200807_145416.log.json) | +| PSPNet | S-101-D8 | 512x512 | 160000 | 14.2 | 13.02 | 45.44 | 46.28 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/resnest/pspnet_s101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k_20200807_145416-a6daa92a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k-20200807_145416.log.json) | +| DeepLabV3 | S-101-D8 | 512x512 | 160000 | 14.6 | 9.28 | 45.71 | 46.59 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/resnest/deeplabv3_s101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k_20200807_144503-17ecabe5.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k-20200807_144503.log.json) | +| DeepLabV3+ | S-101-D8 | 512x512 | 160000 | 16.2 | 11.96 | 46.47 | 47.27 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k_20200807_144503-27b26226.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k-20200807_144503.log.json) | diff --git a/configs/sem_fpn/README.md b/configs/sem_fpn/README.md index c73ade62481..20601147dbd 100644 --- a/configs/sem_fpn/README.md +++ b/configs/sem_fpn/README.md @@ -22,14 +22,14 @@ ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|---------:|----------------|------:|---------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| FPN | R-50 | 512x1024 | 80000 | 2.8 | 13.54 | 74.52 | 76.08 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes_20200717_021437-94018a0d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes-20200717_021437.log.json) | -| FPN | R-101 | 512x1024 | 80000 | 3.9 | 10.29 | 75.80 | 77.40 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes_20200717_012416-c5800d4c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes-20200717_012416.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ---------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| FPN | R-50 | 512x1024 | 80000 | 2.8 | 13.54 | 74.52 | 76.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/sem_fpn/fpn_r50_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes_20200717_021437-94018a0d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes-20200717_021437.log.json) | +| FPN | R-101 | 512x1024 | 80000 | 3.9 | 10.29 | 75.80 | 77.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes_20200717_012416-c5800d4c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes-20200717_012416.log.json) | ### ADE20K -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|--------|----------|-----------|--------:|---------:|----------------|------:|---------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| FPN | R-50 | 512x512 | 160000 | 4.9 | 55.77 | 37.49 | 39.09 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k_20200718_131734-5b5a6ab9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k-20200718_131734.log.json) | -| FPN | R-101 | 512x512 | 160000 | 5.9 | 40.58 | 39.35 | 40.72 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k_20200718_131734-306b5004.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k-20200718_131734.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------ | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| FPN | R-50 | 512x512 | 160000 | 4.9 | 55.77 | 37.49 | 39.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/sem_fpn/fpn_r50_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k_20200718_131734-5b5a6ab9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k-20200718_131734.log.json) | +| FPN | R-101 | 512x512 | 160000 | 5.9 | 40.58 | 39.35 | 40.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/sem_fpn/fpn_r101_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k_20200718_131734-306b5004.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k-20200718_131734.log.json) | diff --git a/configs/unet/README.md b/configs/unet/README.md index d815510a19a..1059815af4d 100644 --- a/configs/unet/README.md +++ b/configs/unet/README.md @@ -19,32 +19,32 @@ ### DRIVE -| Backbone | Head | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Dice | download | -|--------|----------|----------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| UNet-S5-D16 | FCN | 584x565 | 64x64 | 42x42 | 40000 | 0.680 | - | 78.67 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-26cee593.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive-20201223_191051.log.json) | -| UNet-S5-D16 | PSPNet | 584x565 | 64x64 | 42x42 | 40000 | 0.599 | - | 78.62 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive-20201227_181818.log.json) | -| UNet-S5-D16 | DeepLabV3 | 584x565 | 64x64 | 42x42 | 40000 | 0.596 | - | 78.69 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive-20201226_094047.log.json) | +| Backbone | Head | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Dice | config | download | +| ----------- | --------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ----: | ------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| UNet-S5-D16 | FCN | 584x565 | 64x64 | 42x42 | 40000 | 0.680 | - | 78.67 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-26cee593.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive-20201223_191051.log.json) | +| UNet-S5-D16 | PSPNet | 584x565 | 64x64 | 42x42 | 40000 | 0.599 | - | 78.62 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive-20201227_181818.log.json) | +| UNet-S5-D16 | DeepLabV3 | 584x565 | 64x64 | 42x42 | 40000 | 0.596 | - | 78.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive-20201226_094047.log.json) | ### STARE -| Backbone | Head | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Dice | download | -|--------|----------|----------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| UNet-S5-D16 | FCN | 605x700 | 128x128 | 85x85 | 40000 | 0.968 | - | 81.02 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-6ea7cfda.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare-20201223_191051.log.json) | -| UNet-S5-D16 | PSPNet | 605x700 | 128x128 | 85x85 | 40000 | 0.982 | - | 81.22 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare-20201227_181818.log.json) | -| UNet-S5-D16 | DeepLabV3 | 605x700 | 128x128 | 85x85 | 40000 | 0.999 | - | 80.93 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare-20201226_094047.log.json) | +| Backbone | Head | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Dice | config | download | +| ----------- | --------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ----: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| UNet-S5-D16 | FCN | 605x700 | 128x128 | 85x85 | 40000 | 0.968 | - | 81.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-6ea7cfda.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare-20201223_191051.log.json) | +| UNet-S5-D16 | PSPNet | 605x700 | 128x128 | 85x85 | 40000 | 0.982 | - | 81.22 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare-20201227_181818.log.json) | +| UNet-S5-D16 | DeepLabV3 | 605x700 | 128x128 | 85x85 | 40000 | 0.999 | - | 80.93 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare-20201226_094047.log.json) | ### CHASE_DB1 -| Backbone | Head | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Dice | download | -|--------|----------|----------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| UNet-S5-D16 | FCN | 960x999 | 128x128 | 85x85 | 40000 | 0.968 | - | 80.24 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-95852f45.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1-20201223_191051.log.json) | -| UNet-S5-D16 | PSPNet | 960x999 | 128x128 | 85x85 | 40000 | 0.982 | - | 80.36 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1-20201227_181818.log.json) | -| UNet-S5-D16 | DeepLabV3 | 960x999 | 128x128 | 85x85 | 40000 | 0.999 | - | 80.47 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1-20201226_094047.log.json) | +| Backbone | Head | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Dice | download | +| ----------- | --------- | ---------- | --------- | -----: | ------- | -------- | -------------: | ----: | ------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| UNet-S5-D16 | FCN | 960x999 | 128x128 | 85x85 | 40000 | 0.968 | - | 80.24 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-95852f45.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1-20201223_191051.log.json) | +| UNet-S5-D16 | PSPNet | 960x999 | 128x128 | 85x85 | 40000 | 0.982 | - | 80.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1-20201227_181818.log.json) | +| UNet-S5-D16 | DeepLabV3 | 960x999 | 128x128 | 85x85 | 40000 | 0.999 | - | 80.47 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1-20201226_094047.log.json) | ### HRF -| Backbone | Head | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Dice | download | -|--------|----------|----------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| UNet-S5-D16 | FCN | 2336x3504 | 256x256 | 170x170 | 40000 | 2.525 | - | 79.45 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-df3ec8c4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf-20201223_173724.log.json) | -| UNet-S5-D16 | PSPNet | 2336x3504 | 256x256 | 170x170 | 40000 | 2.588 | - | 80.07 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf-20201227_181818.log.json) | -| UNet-S5-D16 | DeepLabV3 | 2336x3504 | 256x256 | 170x170 | 40000 | 2.604 | - | 80.21 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf-20201226_094047.log.json) | +| Backbone | Head | Image Size | Crop Size | Stride | Lr schd | Mem (GB) | Inf time (fps) | Dice | download | +| ----------- | --------- | ---------- | --------- | ------: | ------- | -------- | -------------: | ----: | ------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| UNet-S5-D16 | FCN | 2336x3504 | 256x256 | 170x170 | 40000 | 2.525 | - | 79.45 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-df3ec8c4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf-20201223_173724.log.json) | +| UNet-S5-D16 | PSPNet | 2336x3504 | 256x256 | 170x170 | 40000 | 2.588 | - | 80.07 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf-20201227_181818.log.json) | +| UNet-S5-D16 | DeepLabV3 | 2336x3504 | 256x256 | 170x170 | 40000 | 2.604 | - | 80.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf-20201226_094047.log.json) | diff --git a/configs/upernet/README.md b/configs/upernet/README.md index 4d53a92f9bd..976227bb3fa 100644 --- a/configs/upernet/README.md +++ b/configs/upernet/README.md @@ -18,31 +18,31 @@ ### Cityscapes -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|---------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| UPerNet | R-50 | 512x1024 | 40000 | 6.4 | 4.25 | 77.10 | 78.37 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827.log.json) | -| UPerNet | R-101 | 512x1024 | 40000 | 7.4 | 3.79 | 78.69 | 80.11 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933.log.json) | -| UPerNet | R-50 | 769x769 | 40000 | 7.2 | 1.76 | 77.98 | 79.70 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048.log.json) | -| UPerNet | R-101 | 769x769 | 40000 | 8.4 | 1.56 | 79.03 | 80.77 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819.log.json) | -| UPerNet | R-50 | 512x1024 | 80000 | - | - | 78.19 | 79.19 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207.log.json) | -| UPerNet | R-101 | 512x1024 | 80000 | - | - | 79.40 | 80.46 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403.log.json) | -| UPerNet | R-50 | 769x769 | 80000 | - | - | 79.39 | 80.92 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107.log.json) | -| UPerNet | R-101 | 769x769 | 80000 | - | - | 80.10 | 81.49 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| UPerNet | R-50 | 512x1024 | 40000 | 6.4 | 4.25 | 77.10 | 78.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827.log.json) | +| UPerNet | R-101 | 512x1024 | 40000 | 7.4 | 3.79 | 78.69 | 80.11 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933.log.json) | +| UPerNet | R-50 | 769x769 | 40000 | 7.2 | 1.76 | 77.98 | 79.70 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048.log.json) | +| UPerNet | R-101 | 769x769 | 40000 | 8.4 | 1.56 | 79.03 | 80.77 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_769x769_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819.log.json) | +| UPerNet | R-50 | 512x1024 | 80000 | - | - | 78.19 | 79.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207.log.json) | +| UPerNet | R-101 | 512x1024 | 80000 | - | - | 79.40 | 80.46 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403.log.json) | +| UPerNet | R-50 | 769x769 | 80000 | - | - | 79.39 | 80.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107.log.json) | +| UPerNet | R-101 | 769x769 | 80000 | - | - | 80.10 | 81.49 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_769x769_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014.log.json) | ### ADE20K -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|---------|----------|-----------|--------:|----------|----------------|------:|--------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| UPerNet | R-50 | 512x512 | 80000 | 8.1 | 23.40 | 40.70 | 41.81 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127.log.json) | -| UPerNet | R-101 | 512x512 | 80000 | 9.1 | 20.34 | 42.91 | 43.96 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117.log.json) | -| UPerNet | R-50 | 512x512 | 160000 | - | - | 42.05 | 42.78 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328.log.json) | -| UPerNet | R-101 | 512x512 | 160000 | - | - | 43.82 | 44.85 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| UPerNet | R-50 | 512x512 | 80000 | 8.1 | 23.40 | 40.70 | 41.81 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127.log.json) | +| UPerNet | R-101 | 512x512 | 80000 | 9.1 | 20.34 | 42.91 | 43.96 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117.log.json) | +| UPerNet | R-50 | 512x512 | 160000 | - | - | 42.05 | 42.78 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328.log.json) | +| UPerNet | R-101 | 512x512 | 160000 | - | - | 43.82 | 44.85 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951.log.json) | ### Pascal VOC 2012 + Aug -| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download | -|---------|----------|-----------|--------:|----------|----------------|------:|--------------:|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| UPerNet | R-50 | 512x512 | 20000 | 6.4 | 23.17 | 74.82 | 76.35 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330.log.json) | -| UPerNet | R-101 | 512x512 | 20000 | 7.5 | 19.98 | 77.10 | 78.29 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629.log.json) | -| UPerNet | R-50 | 512x512 | 40000 | - | - | 75.92 | 77.44 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257.log.json) | -| UPerNet | R-101 | 512x512 | 40000 | - | - | 77.43 | 78.56 | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549.log.json) | +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| ------- | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ----------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| UPerNet | R-50 | 512x512 | 20000 | 6.4 | 23.17 | 74.82 | 76.35 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330.log.json) | +| UPerNet | R-101 | 512x512 | 20000 | 7.5 | 19.98 | 77.10 | 78.29 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629.log.json) | +| UPerNet | R-50 | 512x512 | 40000 | - | - | 75.92 | 77.44 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r50_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257.log.json) | +| UPerNet | R-101 | 512x512 | 40000 | - | - | 77.43 | 78.56 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet/upernet_r101_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549.log.json) | diff --git 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"flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": null, - "overflow_y": null, - "overflow_x": null, - "grid_auto_flow": null, - "grid_area": null, - "grid_template_columns": null, - "flex": null, - "_model_name": "LayoutModel", - "justify_items": null, - "grid_row": null, - "max_height": null, - "align_content": null, - "visibility": null, - "align_self": null, - "height": null, - "min_height": null, - "padding": null, - "grid_auto_rows": null, - "grid_gap": null, - "max_width": null, - "order": null, - "_view_module_version": "1.2.0", - "grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null - } - } - } - }, "pycharm": { "stem_cell": { "cell_type": "raw", @@ -286,8 +37,7 @@ { "cell_type": "markdown", "metadata": { - "id": "FVmnaxFJvsb8", - "colab_type": "text" + "id": "FVmnaxFJvsb8" }, "source": [ "# MMSegmentation Tutorial\n", @@ -301,8 +51,7 @@ { "cell_type": "markdown", "metadata": { - "id": "QS8YHrEhbpas", - "colab_type": "text" + "id": "QS8YHrEhbpas" }, "source": [ "## Install MMSegmentation\n", @@ -315,12 +64,10 @@ "cell_type": "code", "metadata": { "id": "UWyLrLYaNEaL", - "colab_type": "code", "colab": { - "base_uri": "https://localhost:8080/", - "height": 170 + "base_uri": "https://localhost:8080/" }, - "outputId": "35b19c63-d6f3-49e1-dcaa-aed3ecd85ed7" + "outputId": "32a47fe3-f10d-47a1-f6b9-b7c235abdab1" }, "source": [ "# Check nvcc version\n", @@ -334,9 +81,10 @@ "output_type": "stream", "text": [ "nvcc: NVIDIA (R) Cuda compiler driver\n", - "Copyright (c) 2005-2019 NVIDIA Corporation\n", - "Built on Sun_Jul_28_19:07:16_PDT_2019\n", - "Cuda compilation tools, release 10.1, V10.1.243\n", + "Copyright (c) 2005-2020 NVIDIA Corporation\n", + "Built on Wed_Jul_22_19:09:09_PDT_2020\n", + "Cuda compilation tools, release 11.0, V11.0.221\n", + "Build cuda_11.0_bu.TC445_37.28845127_0\n", "gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n", "Copyright (C) 2017 Free Software Foundation, Inc.\n", "This is free software; see the source for copying conditions. There is NO\n", @@ -351,12 +99,10 @@ "cell_type": "code", "metadata": { "id": "Ki3WUBjKbutg", - "colab_type": "code", "colab": { - "base_uri": "https://localhost:8080/", - "height": 340 + "base_uri": "https://localhost:8080/" }, - "outputId": "69f42fab-3f44-44d0-bd62-b73836f90a3d" + "outputId": "14bd14b0-4d8c-4fa9-e3f9-da35c0efc0d5" }, "source": [ "# Install PyTorch\n", @@ -370,24 +116,25 @@ "output_type": "stream", "text": [ "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n", - "Requirement already up-to-date: torch==1.5.0+cu101 in /usr/local/lib/python3.6/dist-packages (1.5.0+cu101)\n", - "Requirement already up-to-date: torchvision==0.6.0+cu101 in /usr/local/lib/python3.6/dist-packages (0.6.0+cu101)\n", - "Requirement already satisfied, skipping upgrade: future in /usr/local/lib/python3.6/dist-packages (from torch==1.5.0+cu101) (0.16.0)\n", - "Requirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.6/dist-packages (from torch==1.5.0+cu101) (1.18.5)\n", - "Requirement already satisfied, skipping upgrade: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision==0.6.0+cu101) (7.0.0)\n", + "Requirement already up-to-date: torch==1.5.0+cu101 in /usr/local/lib/python3.7/dist-packages (1.5.0+cu101)\n", + "Requirement already up-to-date: torchvision==0.6.0+cu101 in /usr/local/lib/python3.7/dist-packages (0.6.0+cu101)\n", + "Requirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.7/dist-packages (from torch==1.5.0+cu101) (1.19.5)\n", + "Requirement already satisfied, skipping upgrade: future in /usr/local/lib/python3.7/dist-packages (from torch==1.5.0+cu101) (0.16.0)\n", + "Requirement already satisfied, skipping upgrade: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision==0.6.0+cu101) (7.1.2)\n", "Looking in links: https://download.openmmlab.com/mmcv/dist/index.html\n", "Collecting mmcv-full==latest+torch1.5.0+cu101\n", - " Using cached https://download.openmmlab.com/mmcv/dist/latest/torch1.5.0/cu101/mmcv_full-latest%2Btorch1.5.0%2Bcu101-cp36-cp36m-manylinux1_x86_64.whl\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from mmcv-full==latest+torch1.5.0+cu101) (1.18.5)\n", - "Requirement already satisfied: addict in /usr/local/lib/python3.6/dist-packages (from mmcv-full==latest+torch1.5.0+cu101) (2.2.1)\n", - "Requirement already satisfied: yapf in /usr/local/lib/python3.6/dist-packages (from mmcv-full==latest+torch1.5.0+cu101) (0.30.0)\n", - "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from mmcv-full==latest+torch1.5.0+cu101) (3.13)\n", - "Requirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.6/dist-packages (from mmcv-full==latest+torch1.5.0+cu101) (4.1.2.30)\n", + " Using cached https://download.openmmlab.com/mmcv/dist/1.3.0/torch1.5.0/cu101/mmcv_full-latest%2Btorch1.5.0%2Bcu101-cp37-cp37m-manylinux1_x86_64.whl\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from mmcv-full==latest+torch1.5.0+cu101) (7.1.2)\n", + "Requirement already satisfied: addict in /usr/local/lib/python3.7/dist-packages (from mmcv-full==latest+torch1.5.0+cu101) (2.4.0)\n", + "Requirement already satisfied: yapf in /usr/local/lib/python3.7/dist-packages (from mmcv-full==latest+torch1.5.0+cu101) (0.31.0)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmcv-full==latest+torch1.5.0+cu101) (1.19.5)\n", + "Requirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.7/dist-packages (from mmcv-full==latest+torch1.5.0+cu101) (4.1.2.30)\n", + "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv-full==latest+torch1.5.0+cu101) (3.13)\n", "Installing collected packages: mmcv-full\n", - " Found existing installation: mmcv-full 1.0.0\n", - " Uninstalling mmcv-full-1.0.0:\n", - " Successfully uninstalled mmcv-full-1.0.0\n", - "Successfully installed mmcv-full-1.0.0\n" + " Found existing installation: mmcv-full 1.3.0\n", + " Uninstalling mmcv-full-1.3.0:\n", + " Successfully uninstalled mmcv-full-1.3.0\n", + "Successfully installed mmcv-full-1.3.0\n" ], "name": "stdout" } @@ -397,12 +144,10 @@ "cell_type": "code", "metadata": { "id": "nR-hHRvbNJJZ", - "colab_type": "code", "colab": { - "base_uri": "https://localhost:8080/", - "height": 374 + "base_uri": "https://localhost:8080/" }, - "outputId": "ca6d9c48-0034-47cf-97b5-f31f529cc31c" + "outputId": "10c3b131-d4db-458c-fc10-b94b1c6ed546" }, "source": [ "!rm -rf mmsegmentation\n", @@ -416,26 +161,27 @@ "output_type": "stream", "text": [ "Cloning into 'mmsegmentation'...\n", - "remote: Enumerating objects: 485, done.\u001b[K\n", - "remote: Counting objects: 100% (485/485), done.\u001b[K\n", - "remote: Compressing objects: 100% (303/303), done.\u001b[K\n", - "remote: Total 649 (delta 280), reused 317 (delta 171), pack-reused 164\u001b[K\n", - "Receiving objects: 100% (649/649), 1.96 MiB | 3.99 MiB/s, done.\n", - "Resolving deltas: 100% (364/364), done.\n", + "remote: Enumerating objects: 64, done.\u001B[K\n", + "remote: Counting objects: 100% (64/64), done.\u001B[K\n", + "remote: Compressing objects: 100% (60/60), done.\u001B[K\n", + "remote: Total 2194 (delta 17), reused 12 (delta 4), pack-reused 2130\u001B[K\n", + "Receiving objects: 100% (2194/2194), 3.35 MiB | 26.82 MiB/s, done.\n", + "Resolving deltas: 100% (1536/1536), done.\n", "/content/mmsegmentation\n", "Obtaining file:///content/mmsegmentation\n", - "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from mmseg==0.5.0+b2724da) (3.2.2)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from mmseg==0.5.0+b2724da) (1.18.5)\n", - "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->mmseg==0.5.0+b2724da) (2.4.7)\n", - "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->mmseg==0.5.0+b2724da) (2.8.1)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->mmseg==0.5.0+b2724da) (1.2.0)\n", - "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->mmseg==0.5.0+b2724da) (0.10.0)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib->mmseg==0.5.0+b2724da) (1.12.0)\n", - "Installing collected packages: mmseg\n", - " Found existing installation: mmseg 0.5.0+b2724da\n", - " Can't uninstall 'mmseg'. No files were found to uninstall.\n", - " Running setup.py develop for mmseg\n", - "Successfully installed mmseg\n" + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from mmsegmentation==0.12.0) (3.2.2)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmsegmentation==0.12.0) (1.19.5)\n", + "Requirement already satisfied: terminaltables in /usr/local/lib/python3.7/dist-packages (from mmsegmentation==0.12.0) (3.1.0)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmsegmentation==0.12.0) (2.8.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmsegmentation==0.12.0) (0.10.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmsegmentation==0.12.0) (1.3.1)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmsegmentation==0.12.0) (2.4.7)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->mmsegmentation==0.12.0) (1.15.0)\n", + "Installing collected packages: mmsegmentation\n", + " Found existing installation: mmsegmentation 0.12.0\n", + " Can't uninstall 'mmsegmentation'. No files were found to uninstall.\n", + " Running setup.py develop for mmsegmentation\n", + "Successfully installed mmsegmentation\n" ], "name": "stdout" } @@ -445,12 +191,10 @@ "cell_type": "code", "metadata": { "id": "mAE_h7XhPT7d", - "colab_type": "code", "colab": { - "base_uri": "https://localhost:8080/", - "height": 51 + "base_uri": "https://localhost:8080/" }, - "outputId": "912ec9be-4103-40b8-91cc-4d31e9415f60" + "outputId": "83bf0f8e-fc69-40b1-f9fe-0025724a217c" }, "source": [ "# Check Pytorch installation\n", @@ -467,7 +211,7 @@ "output_type": "stream", "text": [ "1.5.0+cu101 True\n", - "0.5.0+b2724da\n" + "0.12.0\n" ], "name": "stdout" } @@ -476,8 +220,7 @@ { "cell_type": "markdown", "metadata": { - "id": "eUcuC3dUv32I", - "colab_type": "text" + "id": "eUcuC3dUv32I" }, "source": [ "## Run Inference with MMSeg trained weight" @@ -487,12 +230,10 @@ "cell_type": "code", "metadata": { "id": "2hd41IGaiNet", - "colab_type": "code", "colab": { - "base_uri": "https://localhost:8080/", - "height": 204 + "base_uri": "https://localhost:8080/" }, - "outputId": "2834674e-deef-49d7-cd4c-db8dd1ae9733" + "outputId": "b7b2aafc-edf2-43e4-ea43-0b5dd0aa4b4a" }, "source": [ "!mkdir checkpoints\n", @@ -503,16 +244,16 @@ { "output_type": "stream", "text": [ - "--2020-07-09 19:13:21-- https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmsegmentation/models/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth\n", - "Resolving open-mmlab.s3.ap-northeast-2.amazonaws.com (open-mmlab.s3.ap-northeast-2.amazonaws.com)... 52.219.56.140\n", - "Connecting to open-mmlab.s3.ap-northeast-2.amazonaws.com (open-mmlab.s3.ap-northeast-2.amazonaws.com)|52.219.56.140|:443... connected.\n", + "--2021-04-07 22:14:41-- https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmsegmentation/models/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth\n", + "Resolving open-mmlab.s3.ap-northeast-2.amazonaws.com (open-mmlab.s3.ap-northeast-2.amazonaws.com)... 52.219.58.127\n", + "Connecting to open-mmlab.s3.ap-northeast-2.amazonaws.com (open-mmlab.s3.ap-northeast-2.amazonaws.com)|52.219.58.127|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 196205945 (187M) [application/x-www-form-urlencoded]\n", "Saving to: ‘checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth’\n", "\n", - "pspnet_r50-d8_512x1 100%[===================>] 187.12M 11.8MB/s in 18s \n", + "pspnet_r50-d8_512x1 100%[===================>] 187.12M 15.8MB/s in 13s \n", "\n", - "2020-07-09 19:13:40 (10.4 MB/s) - ‘checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth’ saved [196205945/196205945]\n", + "2021-04-07 22:14:54 (14.2 MB/s) - ‘checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth’ saved [196205945/196205945]\n", "\n" ], "name": "stdout" @@ -522,9 +263,7 @@ { "cell_type": "code", "metadata": { - "id": "H8Fxg8i-wHJE", - "colab_type": "code", - "colab": {} + "id": "H8Fxg8i-wHJE" }, "source": [ "from mmseg.apis import inference_segmentor, init_segmentor, show_result_pyplot\n", @@ -536,9 +275,7 @@ { "cell_type": "code", "metadata": { - "id": "umk8sJ0Xuace", - "colab_type": "code", - "colab": {} + "id": "umk8sJ0Xuace" }, "source": [ "config_file = 'configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py'\n", @@ -551,22 +288,30 @@ "cell_type": "code", "metadata": { "id": "nWlQFuTgudxu", - "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5e45f4f6-5bcf-4d04-bb9c-0428ee84a576" }, "source": [ "# build the model from a config file and a checkpoint file\n", "model = init_segmentor(config_file, checkpoint_file, device='cuda:0')" ], "execution_count": 8, - "outputs": [] + "outputs": [ + { + "output_type": "stream", + "text": [ + "Use load_from_local loader\n" + ], + "name": "stdout" + } + ] }, { "cell_type": "code", "metadata": { - "id": "izFv6pSRujk9", - "colab_type": "code", - "colab": {} + "id": "izFv6pSRujk9" }, "source": [ "# test a single image\n", @@ -580,12 +325,11 @@ "cell_type": "code", "metadata": { "id": "bDcs9udgunQK", - "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 504 }, - "outputId": "8221fdb1-92af-4d7c-e65b-c7adf0f5a8af" + "outputId": "7c55f713-4085-47fd-fa06-720a321d0795" }, "source": [ "# show the results\n", @@ -596,7 +340,7 @@ { "output_type": "stream", "text": [ - "/content/mmsegmentation/mmseg/models/segmentors/base.py:265: UserWarning: show==False and out_file is not specified, only result image will be returned\n", + "/content/mmsegmentation/mmseg/models/segmentors/base.py:271: UserWarning: show==False and out_file is not specified, only result image will be returned\n", " warnings.warn('show==False and out_file is not specified, only '\n" ], "name": "stderr" @@ -619,8 +363,7 @@ { "cell_type": "markdown", "metadata": { - "id": "Ta51clKX4cwM", - "colab_type": "text" + "id": "Ta51clKX4cwM" }, "source": [ "## Train a semantic segmentation model on a new dataset\n", @@ -634,8 +377,7 @@ { "cell_type": "markdown", "metadata": { - "id": "AcZg6x_K5Zs3", - "colab_type": "text" + "id": "AcZg6x_K5Zs3" }, "source": [ "### Add a new dataset\n", @@ -652,12 +394,10 @@ "cell_type": "code", "metadata": { "id": "TFIt7MHq5Wls", - "colab_type": "code", "colab": { - "base_uri": "https://localhost:8080/", - "height": 204 + "base_uri": "https://localhost:8080/" }, - "outputId": "5e56d5dc-4f1c-4d7c-f833-51cfdbf8d481" + "outputId": "74a126e4-c8a4-4d2f-a910-b58b71843a23" }, "source": [ "# download and unzip\n", @@ -669,16 +409,16 @@ { "output_type": "stream", "text": [ - "--2020-07-09 19:13:50-- http://dags.stanford.edu/data/iccv09Data.tar.gz\n", + "--2021-04-07 22:15:00-- http://dags.stanford.edu/data/iccv09Data.tar.gz\n", "Resolving dags.stanford.edu (dags.stanford.edu)... 171.64.68.10\n", "Connecting to dags.stanford.edu (dags.stanford.edu)|171.64.68.10|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 14727974 (14M) [application/x-gzip]\n", "Saving to: ‘standford_background.tar.gz’\n", "\n", - "standford_backgroun 100%[===================>] 14.04M 3.22MB/s in 4.4s \n", + "standford_backgroun 100%[===================>] 14.04M 23.4MB/s in 0.6s \n", "\n", - "2020-07-09 19:13:55 (3.22 MB/s) - ‘standford_background.tar.gz’ saved [14727974/14727974]\n", + "2021-04-07 22:15:00 (23.4 MB/s) - ‘standford_background.tar.gz’ saved [14727974/14727974]\n", "\n" ], "name": "stdout" @@ -689,12 +429,11 @@ "cell_type": "code", "metadata": { "id": "78LIci7F9WWI", - "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 377 }, - "outputId": "a7f339c7-a071-40db-f30d-44028dd2ce1c" + "outputId": "c432ddac-5a50-47b1-daac-5a26b07afea2" }, "source": [ "# Let's take a look at the dataset\n", @@ -726,8 +465,7 @@ { "cell_type": "markdown", "metadata": { - "id": "L5mNQuc2GsVE", - "colab_type": "text" + "id": "L5mNQuc2GsVE" }, "source": [ "We need to convert the annotation into semantic map format as an image." @@ -736,9 +474,7 @@ { "cell_type": "code", "metadata": { - "id": "WnGZfribFHCx", - "colab_type": "code", - "colab": {} + "id": "WnGZfribFHCx" }, "source": [ "import os.path as osp\n", @@ -766,12 +502,11 @@ "cell_type": "code", "metadata": { "id": "5MCSS9ABfSks", - "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 377 }, - "outputId": "d093e054-8db3-40e5-a800-061de844597f" + "outputId": "92b9bafc-589e-48fc-c9e9-476f125d6522" }, "source": [ "# Let's take a look at the segmentation map we got\n", @@ -809,9 +544,7 @@ { "cell_type": "code", "metadata": { - "id": "WbeLYCp2k5hl", - "colab_type": "code", - "colab": {} + "id": "WbeLYCp2k5hl" }, "source": [ "# split train/val set randomly\n", @@ -833,8 +566,7 @@ { "cell_type": "markdown", "metadata": { - "id": "HchvmGYB_rrO", - "colab_type": "text" + "id": "HchvmGYB_rrO" }, "source": [ "After downloading the data, we need to implement `load_annotations` function in the new dataset class `StandfordBackgroundDataset`." @@ -843,9 +575,7 @@ { "cell_type": "code", "metadata": { - "id": "LbsWOw62_o-X", - "colab_type": "code", - "colab": {} + "id": "LbsWOw62_o-X" }, "source": [ "from mmseg.datasets.builder import DATASETS\n", @@ -868,8 +598,7 @@ { "cell_type": "markdown", "metadata": { - "id": "yUVtmn3Iq3WA", - "colab_type": "text" + "id": "yUVtmn3Iq3WA" }, "source": [ "### Create a config file\n", @@ -879,9 +608,7 @@ { "cell_type": "code", "metadata": { - "id": "Wwnj9tRzqX_A", - "colab_type": "code", - "colab": {} + "id": "Wwnj9tRzqX_A" }, "source": [ "from mmcv import Config\n", @@ -893,8 +620,7 @@ { "cell_type": "markdown", "metadata": { - "id": "1y2oV5w97jQo", - "colab_type": "text" + "id": "1y2oV5w97jQo" }, "source": [ "Since the given config is used to train PSPNet on cityscapes dataset, we need to modify it accordingly for our new dataset. " @@ -904,12 +630,10 @@ "cell_type": "code", "metadata": { "id": "eyKnYC1Z7iCV", - "colab_type": "code", "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 + "base_uri": "https://localhost:8080/" }, - "outputId": "a25241e2-431c-4944-b0b8-b9c792d5aadd" + "outputId": "6195217b-187f-4675-994b-ba90d8bb3078" }, "source": [ "from mmseg.apis import set_random_seed\n", @@ -991,7 +715,7 @@ "# Set up working dir to save files and logs.\n", "cfg.work_dir = './work_dirs/tutorial'\n", "\n", - "cfg.total_iters = 200\n", + "cfg.runner.max_iters = 200\n", "cfg.log_config.interval = 10\n", "cfg.evaluation.interval = 200\n", "cfg.checkpoint_config.interval = 200\n", @@ -1049,9 +773,9 @@ " norm_cfg=dict(type='BN', requires_grad=True),\n", " align_corners=False,\n", " loss_decode=dict(\n", - " type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))\n", - "train_cfg = dict()\n", - "test_cfg = dict(mode='whole')\n", + " type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),\n", + " train_cfg=dict(),\n", + " test_cfg=dict(mode='whole'))\n", "dataset_type = 'StandfordBackgroundDataset'\n", "data_root = 'iccv09Data'\n", "img_norm_cfg = dict(\n", @@ -1175,7 +899,7 @@ "optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)\n", "optimizer_config = dict()\n", "lr_config = dict(policy='poly', power=0.9, min_lr=0.0001, by_epoch=False)\n", - "total_iters = 200\n", + "runner = dict(type='IterBasedRunner', max_iters=200)\n", "checkpoint_config = dict(by_epoch=False, interval=200)\n", "evaluation = dict(interval=200, metric='mIoU')\n", "work_dir = './work_dirs/tutorial'\n", @@ -1190,8 +914,7 @@ { "cell_type": "markdown", "metadata": { - "id": "QWuH14LYF2gQ", - "colab_type": "text" + "id": "QWuH14LYF2gQ" }, "source": [ "### Train and Evaluation" @@ -1201,22 +924,10 @@ "cell_type": "code", "metadata": { "id": "jYKoSfdMF12B", - "colab_type": "code", "colab": { - "base_uri": "https://localhost:8080/", - "height": 953, - "referenced_widgets": [ - "40a3c0b2c7a44085b69b9c741df20b3e", - "ec96fb4251ea4b8ea268a2bc62b9c75b", - "dae4b284c5a944639991d29f4e79fac5", - "c78567afd0a6418781118ac9f4ecdea9", - "32b7d27a143c41b5bb90f1d8e66a1c67", - "55d75951f51c4ab89e32045c3d6db8a4", - "9d29e2d02731416d9852e9c7c08d1665", - "1bb2b93526cd421aa5d5b86d678932ab" - ] + "base_uri": "https://localhost:8080/" }, - "outputId": "1c0b5a11-434b-4c96-a4aa-9d685fff0856" + "outputId": "422219ca-d7a5-4890-f09f-88c959942e64" }, "source": [ "from mmseg.datasets import build_dataset\n", @@ -1229,7 +940,7 @@ "\n", "# Build the detector\n", "model = build_segmentor(\n", - " cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)\n", + " cfg.model, train_cfg=cfg.get('train_cfg'), test_cfg=cfg.get('test_cfg'))\n", "# Add an attribute for visualization convenience\n", "model.CLASSES = datasets[0].CLASSES\n", "\n", @@ -1243,105 +954,80 @@ { "output_type": "stream", "text": [ - "2020-07-09 19:14:27,264 - mmseg - INFO - Loaded 572 images\n", - "Downloading: \"https://open-mmlab.s3.ap-northeast-2.amazonaws.com/pretrain/third_party/resnet50_v1c-2cccc1ad.pth\" to /root/.cache/torch/checkpoints/resnet50_v1c-2cccc1ad.pth\n" - ], - "name": "stderr" - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "40a3c0b2c7a44085b69b9c741df20b3e", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, max=102567401.0), HTML(value='')))" - ] - }, - "metadata": { - "tags": [] - } - }, - { - "output_type": "stream", - "text": [ - "2020-07-09 19:14:39,770 - mmseg - WARNING - The model and loaded state dict do not match exactly\n", + "/usr/local/lib/python3.7/dist-packages/mmcv/utils/misc.py:304: UserWarning: \"flip_ratio\" is deprecated in `RandomFlip.__init__`, please use \"prob\" instead\n", + " f'\"{src_arg_name}\" is deprecated in '\n", + "2021-04-07 22:15:26,312 - mmseg - INFO - Loaded 572 images\n", + "2021-04-07 22:15:26,915 - mmseg - INFO - Use load_from_openmmlab loader\n", + "2021-04-07 22:15:26,999 - mmseg - WARNING - The model and loaded state dict do not match exactly\n", "\n", "unexpected key in source state_dict: fc.weight, fc.bias\n", "\n", - "2020-07-09 19:14:39,836 - mmseg - INFO - Loaded 143 images\n", - "2020-07-09 19:14:39,837 - mmseg - INFO - load checkpoint from checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth\n" - ], - "name": "stderr" - }, - { - "output_type": "stream", - "text": [ - "\n" - ], - "name": "stdout" - }, - { - "output_type": "stream", - "text": [ - "2020-07-09 19:14:39,990 - mmseg - WARNING - The model and loaded state dict do not match exactly\n", + "2021-04-07 22:15:27,070 - mmseg - INFO - Loaded 143 images\n", + "2021-04-07 22:15:27,072 - mmseg - INFO - load checkpoint from checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth\n", + "2021-04-07 22:15:27,073 - mmseg - INFO - Use load_from_local loader\n", + "2021-04-07 22:15:27,228 - mmseg - WARNING - The model and loaded state dict do not match exactly\n", "\n", "size mismatch for decode_head.conv_seg.weight: copying a param with shape torch.Size([19, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([8, 512, 1, 1]).\n", "size mismatch for decode_head.conv_seg.bias: copying a param with shape torch.Size([19]) from checkpoint, the shape in current model is torch.Size([8]).\n", "size mismatch for auxiliary_head.conv_seg.weight: copying a param with shape torch.Size([19, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([8, 256, 1, 1]).\n", "size mismatch for auxiliary_head.conv_seg.bias: copying a param with shape torch.Size([19]) from checkpoint, the shape in current model is torch.Size([8]).\n", - "2020-07-09 19:14:39,994 - mmseg - INFO - Start running, host: root@71c6cf9b06c5, work_dir: /content/mmsegmentation/work_dirs/tutorial\n", - "2020-07-09 19:14:39,995 - mmseg - INFO - workflow: [('train', 1)], max: 200 iters\n", - "2020-07-09 19:14:54,192 - mmseg - INFO - Iter [10/200]\tlr: 9.598e-03, eta: 0:04:21, time: 1.379, data_time: 0.002, memory: 3772, decode.loss_seg: 1.5616, decode.acc_seg: 46.9241, aux.loss_seg: 0.6853, aux.acc_seg: 38.7292, loss: 2.2469\n", - "2020-07-09 19:15:07,556 - mmseg - INFO - Iter [20/200]\tlr: 9.149e-03, eta: 0:04:04, time: 1.336, data_time: 0.016, memory: 3772, decode.loss_seg: 0.8215, decode.acc_seg: 68.8879, aux.loss_seg: 0.5371, aux.acc_seg: 67.9098, loss: 1.3586\n", - "2020-07-09 19:15:20,914 - mmseg - INFO - Iter [30/200]\tlr: 8.698e-03, eta: 0:03:49, time: 1.336, data_time: 0.016, memory: 3772, decode.loss_seg: 0.5890, decode.acc_seg: 66.6747, aux.loss_seg: 0.3591, aux.acc_seg: 65.8590, loss: 0.9481\n", - "2020-07-09 19:15:34,235 - mmseg - INFO - Iter [40/200]\tlr: 8.244e-03, eta: 0:03:35, time: 1.332, data_time: 0.016, memory: 3772, decode.loss_seg: 0.5888, decode.acc_seg: 71.6006, aux.loss_seg: 0.3192, aux.acc_seg: 66.5800, loss: 0.9079\n", - "2020-07-09 19:15:47,580 - mmseg - INFO - Iter [50/200]\tlr: 7.788e-03, eta: 0:03:21, time: 1.335, data_time: 0.016, memory: 3772, decode.loss_seg: 0.7011, decode.acc_seg: 65.8105, aux.loss_seg: 0.3223, aux.acc_seg: 62.9866, loss: 1.0235\n", - "2020-07-09 19:16:00,900 - mmseg - INFO - Iter [60/200]\tlr: 7.328e-03, eta: 0:03:07, time: 1.332, data_time: 0.016, memory: 3772, decode.loss_seg: 0.5531, decode.acc_seg: 66.3968, aux.loss_seg: 0.2624, aux.acc_seg: 63.4624, loss: 0.8156\n", - "2020-07-09 19:16:14,199 - mmseg - INFO - Iter [70/200]\tlr: 6.865e-03, eta: 0:02:54, time: 1.330, data_time: 0.016, memory: 3772, decode.loss_seg: 0.5888, decode.acc_seg: 66.5814, aux.loss_seg: 0.2905, aux.acc_seg: 62.6161, loss: 0.8792\n", - "2020-07-09 19:16:28,148 - mmseg - INFO - Iter [80/200]\tlr: 6.398e-03, eta: 0:02:41, time: 1.395, data_time: 0.016, memory: 3772, decode.loss_seg: 0.4988, decode.acc_seg: 69.7736, aux.loss_seg: 0.2388, aux.acc_seg: 68.5068, loss: 0.7376\n", - "2020-07-09 19:16:41,440 - mmseg - INFO - Iter [90/200]\tlr: 5.928e-03, eta: 0:02:27, time: 1.330, data_time: 0.016, memory: 3772, decode.loss_seg: 0.5177, decode.acc_seg: 72.9874, aux.loss_seg: 0.2512, aux.acc_seg: 71.1549, loss: 0.7690\n", - "2020-07-09 19:16:54,703 - mmseg - INFO - Iter [100/200]\tlr: 5.453e-03, eta: 0:02:14, time: 1.326, data_time: 0.016, memory: 3772, decode.loss_seg: 0.5794, decode.acc_seg: 65.9114, aux.loss_seg: 0.2557, aux.acc_seg: 65.2695, loss: 0.8351\n", - "2020-07-09 19:17:07,972 - mmseg - INFO - Iter [110/200]\tlr: 4.974e-03, eta: 0:02:00, time: 1.327, data_time: 0.016, memory: 3772, decode.loss_seg: 0.5395, decode.acc_seg: 69.2955, aux.loss_seg: 0.2443, aux.acc_seg: 68.5840, loss: 0.7838\n", - "2020-07-09 19:17:21,227 - mmseg - INFO - Iter [120/200]\tlr: 4.489e-03, eta: 0:01:47, time: 1.326, data_time: 0.016, memory: 3772, decode.loss_seg: 0.5568, decode.acc_seg: 70.1717, aux.loss_seg: 0.2490, aux.acc_seg: 69.4707, loss: 0.8058\n", - "2020-07-09 19:17:34,513 - mmseg - INFO - Iter [130/200]\tlr: 3.998e-03, eta: 0:01:33, time: 1.328, data_time: 0.016, memory: 3772, decode.loss_seg: 0.5222, decode.acc_seg: 72.1791, aux.loss_seg: 0.2446, aux.acc_seg: 71.0046, loss: 0.7668\n", - "2020-07-09 19:17:47,812 - mmseg - INFO - Iter [140/200]\tlr: 3.500e-03, eta: 0:01:20, time: 1.330, data_time: 0.016, memory: 3772, decode.loss_seg: 0.5178, decode.acc_seg: 72.7657, aux.loss_seg: 0.2552, aux.acc_seg: 70.8837, loss: 0.7730\n", - "2020-07-09 19:18:01,667 - mmseg - INFO - Iter [150/200]\tlr: 2.994e-03, eta: 0:01:07, time: 1.386, data_time: 0.016, memory: 3772, decode.loss_seg: 0.4719, decode.acc_seg: 72.4819, aux.loss_seg: 0.2263, aux.acc_seg: 69.9169, loss: 0.6982\n", - "2020-07-09 19:18:14,904 - mmseg - INFO - Iter [160/200]\tlr: 2.478e-03, eta: 0:00:53, time: 1.324, data_time: 0.016, memory: 3772, decode.loss_seg: 0.4494, decode.acc_seg: 75.4808, aux.loss_seg: 0.2228, aux.acc_seg: 73.2249, loss: 0.6723\n", - "2020-07-09 19:18:28,151 - mmseg - INFO - Iter [170/200]\tlr: 1.949e-03, eta: 0:00:40, time: 1.325, data_time: 0.016, memory: 3772, decode.loss_seg: 0.4412, decode.acc_seg: 72.4503, aux.loss_seg: 0.2177, aux.acc_seg: 69.9681, loss: 0.6589\n", - "2020-07-09 19:18:41,413 - mmseg - INFO - Iter [180/200]\tlr: 1.402e-03, eta: 0:00:26, time: 1.326, data_time: 0.016, memory: 3772, decode.loss_seg: 0.4127, decode.acc_seg: 74.4395, aux.loss_seg: 0.1955, aux.acc_seg: 72.5129, loss: 0.6082\n", - "2020-07-09 19:18:54,678 - mmseg - INFO - Iter [190/200]\tlr: 8.277e-04, eta: 0:00:13, time: 1.326, data_time: 0.016, memory: 3772, decode.loss_seg: 0.4733, decode.acc_seg: 74.7937, aux.loss_seg: 0.2285, aux.acc_seg: 72.0337, loss: 0.7019\n", - "2020-07-09 19:19:07,808 - mmseg - INFO - Saving checkpoint at 200 iterations\n" + "2021-04-07 22:15:27,232 - mmseg - INFO - Start running, host: root@c8cc0e0b80dc, work_dir: /content/mmsegmentation/work_dirs/tutorial\n", + "2021-04-07 22:15:27,237 - mmseg - INFO - workflow: [('train', 1)], max: 200 iters\n", + "2021-04-07 22:15:33,883 - mmseg - INFO - Iter [10/200]\tlr: 9.598e-03, eta: 0:01:58, time: 0.626, data_time: 0.039, memory: 3772, decode.loss_seg: 1.5570, decode.acc_seg: 44.2138, aux.loss_seg: 0.6808, aux.acc_seg: 40.7060, loss: 2.2378\n", + "2021-04-07 22:15:39,777 - mmseg - INFO - Iter [20/200]\tlr: 9.149e-03, eta: 0:01:49, time: 0.589, data_time: 0.008, memory: 3772, decode.loss_seg: 0.8328, decode.acc_seg: 67.4587, aux.loss_seg: 0.5270, aux.acc_seg: 65.5612, loss: 1.3598\n", + "2021-04-07 22:15:45,723 - mmseg - INFO - Iter [30/200]\tlr: 8.698e-03, eta: 0:01:42, time: 0.595, data_time: 0.008, memory: 3772, decode.loss_seg: 0.6151, decode.acc_seg: 65.5550, aux.loss_seg: 0.3798, aux.acc_seg: 64.0860, loss: 0.9949\n", + "2021-04-07 22:15:51,759 - mmseg - INFO - Iter [40/200]\tlr: 8.244e-03, eta: 0:01:36, time: 0.603, data_time: 0.008, memory: 3772, decode.loss_seg: 0.5840, decode.acc_seg: 68.8598, aux.loss_seg: 0.3035, aux.acc_seg: 66.3350, loss: 0.8875\n", + "2021-04-07 22:15:57,851 - mmseg - INFO - Iter [50/200]\tlr: 7.788e-03, eta: 0:01:30, time: 0.609, data_time: 0.008, memory: 3772, decode.loss_seg: 0.5198, decode.acc_seg: 69.1188, aux.loss_seg: 0.2708, aux.acc_seg: 66.1400, loss: 0.7906\n", + "2021-04-07 22:16:04,047 - mmseg - INFO - Iter [60/200]\tlr: 7.328e-03, eta: 0:01:24, time: 0.620, data_time: 0.008, memory: 3772, decode.loss_seg: 0.7124, decode.acc_seg: 66.1938, aux.loss_seg: 0.3291, aux.acc_seg: 63.7193, loss: 1.0415\n", + "2021-04-07 22:16:10,183 - mmseg - INFO - Iter [70/200]\tlr: 6.865e-03, eta: 0:01:19, time: 0.614, data_time: 0.008, memory: 3772, decode.loss_seg: 0.6217, decode.acc_seg: 67.6348, aux.loss_seg: 0.2921, aux.acc_seg: 65.4327, loss: 0.9138\n", + "2021-04-07 22:16:16,975 - mmseg - INFO - Iter [80/200]\tlr: 6.398e-03, eta: 0:01:14, time: 0.679, data_time: 0.083, memory: 3772, decode.loss_seg: 0.5825, decode.acc_seg: 67.3635, aux.loss_seg: 0.2740, aux.acc_seg: 66.0855, loss: 0.8565\n", + "2021-04-07 22:16:22,951 - mmseg - INFO - Iter [90/200]\tlr: 5.928e-03, eta: 0:01:07, time: 0.598, data_time: 0.008, memory: 3772, decode.loss_seg: 0.5509, decode.acc_seg: 71.3504, aux.loss_seg: 0.2507, aux.acc_seg: 70.5064, loss: 0.8016\n", + "2021-04-07 22:16:28,880 - mmseg - INFO - Iter [100/200]\tlr: 5.453e-03, eta: 0:01:01, time: 0.593, data_time: 0.008, memory: 3772, decode.loss_seg: 0.6903, decode.acc_seg: 62.3287, aux.loss_seg: 0.3010, aux.acc_seg: 62.1792, loss: 0.9913\n", + "2021-04-07 22:16:34,786 - mmseg - INFO - Iter [110/200]\tlr: 4.974e-03, eta: 0:00:54, time: 0.591, data_time: 0.008, memory: 3772, decode.loss_seg: 0.5627, decode.acc_seg: 68.7782, aux.loss_seg: 0.2505, aux.acc_seg: 68.3666, loss: 0.8132\n", + "2021-04-07 22:16:40,679 - mmseg - INFO - Iter [120/200]\tlr: 4.489e-03, eta: 0:00:48, time: 0.589, data_time: 0.008, memory: 3772, decode.loss_seg: 0.5006, decode.acc_seg: 70.7204, aux.loss_seg: 0.2400, aux.acc_seg: 69.5582, loss: 0.7406\n", + "2021-04-07 22:16:46,554 - mmseg - INFO - Iter [130/200]\tlr: 3.998e-03, eta: 0:00:42, time: 0.588, data_time: 0.008, memory: 3772, decode.loss_seg: 0.4775, decode.acc_seg: 70.6324, aux.loss_seg: 0.2211, aux.acc_seg: 69.0519, loss: 0.6986\n", + "2021-04-07 22:16:52,442 - mmseg - INFO - Iter [140/200]\tlr: 3.500e-03, eta: 0:00:36, time: 0.589, data_time: 0.008, memory: 3772, decode.loss_seg: 0.4812, decode.acc_seg: 71.5263, aux.loss_seg: 0.2262, aux.acc_seg: 68.9376, loss: 0.7074\n", + "2021-04-07 22:16:59,045 - mmseg - INFO - Iter [150/200]\tlr: 2.994e-03, eta: 0:00:30, time: 0.660, data_time: 0.075, memory: 3772, decode.loss_seg: 0.4366, decode.acc_seg: 73.8778, aux.loss_seg: 0.2085, aux.acc_seg: 71.9269, loss: 0.6452\n", + "2021-04-07 22:17:04,994 - mmseg - INFO - Iter [160/200]\tlr: 2.478e-03, eta: 0:00:24, time: 0.595, data_time: 0.008, memory: 3772, decode.loss_seg: 0.4244, decode.acc_seg: 73.4474, aux.loss_seg: 0.1975, aux.acc_seg: 72.5327, loss: 0.6219\n", + "2021-04-07 22:17:10,945 - mmseg - INFO - Iter [170/200]\tlr: 1.949e-03, eta: 0:00:18, time: 0.595, data_time: 0.008, memory: 3772, decode.loss_seg: 0.4268, decode.acc_seg: 71.7624, aux.loss_seg: 0.2042, aux.acc_seg: 70.3237, loss: 0.6311\n", + "2021-04-07 22:17:16,919 - mmseg - INFO - Iter [180/200]\tlr: 1.402e-03, eta: 0:00:12, time: 0.597, data_time: 0.008, memory: 3772, decode.loss_seg: 0.4488, decode.acc_seg: 72.1597, aux.loss_seg: 0.2177, aux.acc_seg: 70.9026, loss: 0.6665\n", + "2021-04-07 22:17:22,916 - mmseg - INFO - Iter [190/200]\tlr: 8.277e-04, eta: 0:00:06, time: 0.600, data_time: 0.008, memory: 3772, decode.loss_seg: 0.4651, decode.acc_seg: 75.1950, aux.loss_seg: 0.2244, aux.acc_seg: 73.2528, loss: 0.6894\n", + "2021-04-07 22:17:28,838 - mmseg - INFO - Saving checkpoint at 200 iterations\n" ], "name": "stderr" }, { "output_type": "stream", "text": [ - "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 143/143, 10.9 task/s, elapsed: 13s, ETA: 0s" + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 143/143, 27.5 task/s, elapsed: 5s, ETA: 0s" ], "name": "stdout" }, { "output_type": "stream", "text": [ - "2020-07-09 19:19:22,647 - mmseg - INFO - per class results:\n", - "Class IoU Acc\n", - "sky 88.67 94.28\n", - "tree 68.95 86.73\n", - "road 86.23 94.42\n", - "grass 70.01 91.35\n", - "water 62.08 68.32\n", - "bldg 81.11 88.89\n", - "mntn 0.00 0.00\n", - "fg obj 70.39 82.49\n", - "Summary:\n", - "Scope mIoU mAcc aAcc\n", - "global 65.93 75.81 87.48\n", - "\n", - "2020-07-09 19:19:22,660 - mmseg - INFO - Iter [200/200]\tlr: 1.841e-04, mIoU: 0.6593, mAcc: 0.7581, aAcc: 0.8748\n" + "2021-04-07 22:17:35,967 - mmseg - INFO - per class results:\n", + "2021-04-07 22:17:35,969 - mmseg - INFO - \n", + "+--------+-------+-------+\n", + "| Class | IoU | Acc |\n", + "+--------+-------+-------+\n", + "| sky | 87.18 | 91.91 |\n", + "| tree | 69.54 | 90.08 |\n", + "| road | 84.38 | 92.03 |\n", + "| grass | 72.91 | 90.34 |\n", + "| water | 57.42 | 62.66 |\n", + "| bldg | 78.36 | 87.32 |\n", + "| mntn | 0.0 | 0.0 |\n", + "| fg obj | 67.42 | 82.39 |\n", + "+--------+-------+-------+\n", + "2021-04-07 22:17:35,974 - mmseg - INFO - Summary:\n", + "2021-04-07 22:17:35,976 - mmseg - INFO - \n", + "+--------+-------+-------+-------+\n", + "| Scope | mIoU | mAcc | aAcc |\n", + "+--------+-------+-------+-------+\n", + "| global | 64.65 | 74.59 | 85.92 |\n", + "+--------+-------+-------+-------+\n", + "2021-04-07 22:17:35,986 - mmseg - INFO - Iter(val) [200]\tmIoU: 0.6465, mAcc: 0.7459, aAcc: 0.8592, IoU.sky: 0.8718, IoU.tree: 0.6954, IoU.road: 0.8438, IoU.grass: 0.7291, IoU.water: 0.5742, IoU.bldg: 0.7836, IoU.mntn: 0.0000, IoU.fg obj: 0.6742, Acc.sky: 0.9191, Acc.tree: 0.9008, Acc.road: 0.9203, Acc.grass: 0.9034, Acc.water: 0.6266, Acc.bldg: 0.8732, Acc.mntn: 0.0000, Acc.fg obj: 0.8239\n" ], "name": "stderr" } @@ -1350,8 +1036,7 @@ { "cell_type": "markdown", "metadata": { - "id": "DEkWOP-NMbc_", - "colab_type": "text" + "id": "DEkWOP-NMbc_" }, "source": [ "Inference with trained model" @@ -1361,12 +1046,11 @@ "cell_type": "code", "metadata": { "id": "ekG__UfaH_OU", - "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 645 }, - "outputId": "ac1eb835-19ed-48e6-8f77-e6d325b915c4" + "outputId": "1437419c-869a-4902-df86-d4f6f8b2597a" }, "source": [ "img = mmcv.imread('iccv09Data/images/6000124.jpg')\n", @@ -1381,7 +1065,7 @@ { "output_type": "stream", "text": [ - "/content/mmsegmentation/mmseg/models/segmentors/base.py:265: UserWarning: show==False and out_file is not specified, only result image will be returned\n", + "/content/mmsegmentation/mmseg/models/segmentors/base.py:271: UserWarning: show==False and out_file is not specified, only result image will be returned\n", " warnings.warn('show==False and out_file is not specified, only '\n" ], "name": "stderr" @@ -1400,7 +1084,7 @@ { "output_type": "display_data", "data": { - "image/png": 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\n", 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\n", 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" ] diff --git a/docs/useful_tools.md b/docs/useful_tools.md index 7b2e3fde1e5..81cbeb86628 100644 --- a/docs/useful_tools.md +++ b/docs/useful_tools.md @@ -46,10 +46,44 @@ The final output filename will be `psp_r50_512x1024_40ki_cityscapes-{hash id}.pt We provide a script to convert model to [ONNX](https://github.com/onnx/onnx) format. The converted model could be visualized by tools like [Netron](https://github.com/lutzroeder/netron). Besides, we also support comparing the output results between Pytorch and ONNX model. +```bash +python tools/pytorch2onnx.py \ + ${CONFIG_FILE} \ + --checkpoint ${CHECKPOINT_FILE} \ + --output-file ${ONNX_FILE} \ + --input-img ${INPUT_IMG} \ + --shape ${INPUT_SHAPE} \ + --show \ + --verify \ + --dynamic-export \ + --cfg-options \ + model.test_cfg.mode="whole" +``` + +Description of arguments: + +- `config` : The path of a model config file. +- `--checkpoint` : The path of a model checkpoint file. +- `--output-file`: The path of output ONNX model. If not specified, it will be set to `tmp.onnx`. +- `--input-img` : The path of an input image for conversion and visualize. +- `--shape`: The height and width of input tensor to the model. If not specified, it will be set to `256 256`. +- `--show`: Determines whether to print the architecture of the exported model. If not specified, it will be set to `False`. +- `--verify`: Determines whether to verify the correctness of an exported model. If not specified, it will be set to `False`. +- `--dynamic-export`: Determines whether to export ONNX model with dynamic input and output shapes. If not specified, it will be set to `False`. +- `--cfg-options`:Update config options. + +**Note**: This tool is still experimental. Some customized operators are not supported for now. + +### Convert to TorchScript (experimental) + +We also provide a script to convert model to [TorchScript](https://pytorch.org/docs/stable/jit.html) format. You can use the pytorch C++ API [LibTorch](https://pytorch.org/docs/stable/cpp_index.html) inference the trained model. The converted model could be visualized by tools like [Netron](https://github.com/lutzroeder/netron). Besides, we also support comparing the output results between Pytorch and TorchScript model. + ```shell -python tools/pytorch2onnx.py ${CONFIG_FILE} --checkpoint ${CHECKPOINT_FILE} --output-file ${ONNX_FILE} [--shape ${INPUT_SHAPE} --verify] +python tools/pytorch2torchscript.py ${CONFIG_FILE} --checkpoint ${CHECKPOINT_FILE} --output-file ${ONNX_FILE} [--shape ${INPUT_SHAPE} --verify] ``` +**Note**: It's only support PyTorch>=1.8.0 for now. + **Note**: This tool is still experimental. Some customized operators are not supported for now. ## Miscellaneous diff --git a/mmseg/apis/inference.py b/mmseg/apis/inference.py index 9052cdd32a0..bf875cb2625 100644 --- a/mmseg/apis/inference.py +++ b/mmseg/apis/inference.py @@ -103,7 +103,9 @@ def show_result_pyplot(model, result, palette=None, fig_size=(15, 10), - opacity=0.5): + opacity=0.5, + title='', + block=True): """Visualize the segmentation results on the image. Args: @@ -117,6 +119,10 @@ def show_result_pyplot(model, opacity(float): Opacity of painted segmentation map. Default 0.5. Must be in (0, 1] range. + title (str): The title of pyplot figure. + Default is ''. + block (bool): Whether to block the pyplot figure. + Default is True. """ if hasattr(model, 'module'): model = model.module @@ -124,4 +130,6 @@ def show_result_pyplot(model, img, result, palette=palette, show=False, opacity=opacity) plt.figure(figsize=fig_size) plt.imshow(mmcv.bgr2rgb(img)) - plt.show() + plt.title(title) + plt.tight_layout() + plt.show(block=block) diff --git a/mmseg/core/evaluation/metrics.py b/mmseg/core/evaluation/metrics.py index fb07fc44d17..c5924a4c861 100644 --- a/mmseg/core/evaluation/metrics.py +++ b/mmseg/core/evaluation/metrics.py @@ -57,11 +57,11 @@ def intersect_and_union(pred_label, intersect = pred_label[pred_label == label] area_intersect = torch.histc( - intersect.float(), bins=num_classes, min=0, max=num_classes - 1) + intersect.float(), bins=(num_classes), min=0, max=num_classes - 1) area_pred_label = torch.histc( - pred_label.float(), bins=num_classes, min=0, max=num_classes - 1) + pred_label.float(), bins=(num_classes), min=0, max=num_classes - 1) area_label = torch.histc( - label.float(), bins=num_classes, min=0, max=num_classes - 1) + label.float(), bins=(num_classes), min=0, max=num_classes - 1) area_union = area_pred_label + area_label - area_intersect return area_intersect, area_union, area_pred_label, area_label diff --git a/mmseg/models/losses/accuracy.py b/mmseg/models/losses/accuracy.py index e45f9ec4857..c0fd2e7e74a 100644 --- a/mmseg/models/losses/accuracy.py +++ b/mmseg/models/losses/accuracy.py @@ -44,7 +44,7 @@ def accuracy(pred, target, topk=1, thresh=None): correct = correct & (pred_value > thresh).t() res = [] for k in topk: - correct_k = correct[:k].view(-1).float().sum(0, keepdim=True) + correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True) res.append(correct_k.mul_(100.0 / target.numel())) return res[0] if return_single else res diff --git a/mmseg/models/segmentors/__init__.py b/mmseg/models/segmentors/__init__.py index 3f600ecb9fa..dca2f094053 100644 --- a/mmseg/models/segmentors/__init__.py +++ b/mmseg/models/segmentors/__init__.py @@ -1,4 +1,5 @@ +from .base import BaseSegmentor from .cascade_encoder_decoder import CascadeEncoderDecoder from .encoder_decoder import EncoderDecoder -__all__ = ['EncoderDecoder', 'CascadeEncoderDecoder'] +__all__ = ['BaseSegmentor', 'EncoderDecoder', 'CascadeEncoderDecoder'] diff --git a/mmseg/models/segmentors/encoder_decoder.py b/mmseg/models/segmentors/encoder_decoder.py index 2284906e3fc..b2d067dcbed 100644 --- a/mmseg/models/segmentors/encoder_decoder.py +++ b/mmseg/models/segmentors/encoder_decoder.py @@ -216,9 +216,14 @@ def whole_inference(self, img, img_meta, rescale): seg_logit = self.encode_decode(img, img_meta) if rescale: + # support dynamic shape for onnx + if torch.onnx.is_in_onnx_export(): + size = img.shape[2:] + else: + size = img_meta[0]['ori_shape'][:2] seg_logit = resize( seg_logit, - size=img_meta[0]['ori_shape'][:2], + size=size, mode='bilinear', align_corners=self.align_corners, warning=False) diff --git a/mmseg/ops/wrappers.py b/mmseg/ops/wrappers.py index a6d755273df..0ed9a0cb8d7 100644 --- a/mmseg/ops/wrappers.py +++ b/mmseg/ops/wrappers.py @@ -1,6 +1,5 @@ import warnings -import torch import torch.nn as nn import torch.nn.functional as F @@ -24,8 +23,6 @@ def resize(input, 'the output would more aligned if ' f'input size {(input_h, input_w)} is `x+1` and ' f'out size {(output_h, output_w)} is `nx+1`') - if isinstance(size, torch.Size): - size = tuple(int(x) for x in size) return F.interpolate(input, size, scale_factor, mode, align_corners) diff --git a/tests/test_metrics.py b/tests/test_metrics.py index 2033617c2a5..b50e165926a 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -64,7 +64,11 @@ def test_metrics(): ignore_index = 255 results = np.random.randint(0, num_classes, size=pred_size) label = np.random.randint(0, num_classes, size=pred_size) + + # Test the availability of arg: ignore_index. label[:, 2, 5:10] = ignore_index + + # Test the correctness of the implementation of mIoU calculation. all_acc, acc, iou = eval_metrics( results, label, num_classes, ignore_index, metrics='mIoU') all_acc_l, acc_l, iou_l = legacy_mean_iou(results, label, num_classes, @@ -72,7 +76,7 @@ def test_metrics(): assert all_acc == all_acc_l assert np.allclose(acc, acc_l) assert np.allclose(iou, iou_l) - + # Test the correctness of the implementation of mDice calculation. all_acc, acc, dice = eval_metrics( results, label, num_classes, ignore_index, metrics='mDice') all_acc_l, acc_l, dice_l = legacy_mean_dice(results, label, num_classes, @@ -80,7 +84,7 @@ def test_metrics(): assert all_acc == all_acc_l assert np.allclose(acc, acc_l) assert np.allclose(dice, dice_l) - + # Test the correctness of the implementation of joint calculation. all_acc, acc, iou, dice = eval_metrics( results, label, num_classes, ignore_index, metrics=['mIoU', 'mDice']) assert all_acc == all_acc_l @@ -88,6 +92,8 @@ def test_metrics(): assert np.allclose(iou, iou_l) assert np.allclose(dice, dice_l) + # Test the correctness of calculation when arg: num_classes is larger + # than the maximum value of input maps. results = np.random.randint(0, 5, size=pred_size) label = np.random.randint(0, 4, size=pred_size) all_acc, acc, iou = eval_metrics( @@ -121,6 +127,17 @@ def test_metrics(): assert dice[-1] == -1 assert iou[-1] == -1 + # Test the bug which is caused by torch.histc. + # torch.histc: https://pytorch.org/docs/stable/generated/torch.histc.html + # When the arg:bins is set to be same as arg:max, + # some channels of mIoU may be nan. + results = np.array([np.repeat(31, 59)]) + label = np.array([np.arange(59)]) + num_classes = 59 + all_acc, acc, iou = eval_metrics( + results, label, num_classes, ignore_index=255, metrics='mIoU') + assert not np.any(np.isnan(iou)) + def test_mean_iou(): pred_size = (10, 30, 30) @@ -182,7 +199,7 @@ def save_arr(input_arrays: list, title: str, is_image: bool, dir: str): filenames.append(filename) return filenames - pred_size = (10, 512, 1024) + pred_size = (10, 30, 30) num_classes = 19 ignore_index = 255 results = np.random.randint(0, num_classes, size=pred_size) diff --git a/tools/pytorch2onnx.py b/tools/pytorch2onnx.py index 2ec9feb59a8..71f1bb7227e 100644 --- a/tools/pytorch2onnx.py +++ b/tools/pytorch2onnx.py @@ -7,10 +7,14 @@ import torch import torch._C import torch.serialization +from mmcv import DictAction from mmcv.onnx import register_extra_symbolics from mmcv.runner import load_checkpoint from torch import nn +from mmseg.apis import show_result_pyplot +from mmseg.apis.inference import LoadImage +from mmseg.datasets.pipelines import Compose from mmseg.models import build_segmentor torch.manual_seed(3) @@ -67,25 +71,61 @@ def _demo_mm_inputs(input_shape, num_classes): return mm_inputs +def _prepare_input_img(img_path, test_pipeline, shape=None): + # build the data pipeline + if shape is not None: + test_pipeline[1]['img_scale'] = shape + test_pipeline[1]['transforms'][0]['keep_ratio'] = False + test_pipeline = [LoadImage()] + test_pipeline[1:] + test_pipeline = Compose(test_pipeline) + # prepare data + data = dict(img=img_path) + data = test_pipeline(data) + imgs = data['img'] + img_metas = [i.data for i in data['img_metas']] + + mm_inputs = {'imgs': imgs, 'img_metas': img_metas} + + return mm_inputs + + +def _update_input_img(img_list, img_meta_list): + # update img and its meta list + N, C, H, W = img_list[0].shape + img_meta = img_meta_list[0][0] + new_img_meta_list = [[{ + 'img_shape': (H, W, C), + 'ori_shape': (H, W, C), + 'pad_shape': (H, W, C), + 'filename': img_meta['filename'], + 'scale_factor': 1., + 'flip': False, + } for _ in range(N)]] + + return img_list, new_img_meta_list + + def pytorch2onnx(model, - input_shape, + mm_inputs, opset_version=11, show=False, output_file='tmp.onnx', - verify=False): + verify=False, + dynamic_export=False): """Export Pytorch model to ONNX model and verify the outputs are same between Pytorch and ONNX. Args: model (nn.Module): Pytorch model we want to export. - input_shape (tuple): Use this input shape to construct - the corresponding dummy input and execute the model. + mm_inputs (dict): Contain the input tensors and img_metas information. opset_version (int): The onnx op version. Default: 11. show (bool): Whether print the computation graph. Default: False. output_file (string): The path to where we store the output ONNX model. Default: `tmp.onnx`. verify (bool): Whether compare the outputs between Pytorch and ONNX. Default: False. + dynamic_export (bool): Whether to export ONNX with dynamic axis. + Default: False. """ model.cpu().eval() @@ -94,28 +134,45 @@ def pytorch2onnx(model, else: num_classes = model.decode_head.num_classes - mm_inputs = _demo_mm_inputs(input_shape, num_classes) - imgs = mm_inputs.pop('imgs') img_metas = mm_inputs.pop('img_metas') + ori_shape = img_metas[0]['ori_shape'] img_list = [img[None, :] for img in imgs] img_meta_list = [[img_meta] for img_meta in img_metas] + img_list, img_meta_list = _update_input_img(img_list, img_meta_list) # replace original forward function origin_forward = model.forward model.forward = partial( model.forward, img_metas=img_meta_list, return_loss=False) + dynamic_axes = None + if dynamic_export: + dynamic_axes = { + 'input': { + 0: 'batch', + 2: 'height', + 3: 'width' + }, + 'output': { + 1: 'batch', + 2: 'height', + 3: 'width' + } + } register_extra_symbolics(opset_version) with torch.no_grad(): torch.onnx.export( model, (img_list, ), output_file, + input_names=['input'], + output_names=['output'], export_params=True, - keep_initializers_as_inputs=True, + keep_initializers_as_inputs=False, verbose=show, - opset_version=opset_version) + opset_version=opset_version, + dynamic_axes=dynamic_axes) print(f'Successfully exported ONNX model: {output_file}') model.forward = origin_forward @@ -125,9 +182,28 @@ def pytorch2onnx(model, onnx_model = onnx.load(output_file) onnx.checker.check_model(onnx_model) + if dynamic_export: + # scale image for dynamic shape test + img_list = [ + nn.functional.interpolate(_, scale_factor=1.5) + for _ in img_list + ] + # concate flip image for batch test + flip_img_list = [_.flip(-1) for _ in img_list] + img_list = [ + torch.cat((ori_img, flip_img), 0) + for ori_img, flip_img in zip(img_list, flip_img_list) + ] + + # update img_meta + img_list, img_meta_list = _update_input_img( + img_list, img_meta_list) + # check the numerical value # get pytorch output - pytorch_result = model(img_list, img_meta_list, return_loss=False)[0] + with torch.no_grad(): + pytorch_result = model(img_list, img_meta_list, return_loss=False) + pytorch_result = np.stack(pytorch_result, 0) # get onnx output input_all = [node.name for node in onnx_model.graph.input] @@ -138,10 +214,42 @@ def pytorch2onnx(model, assert (len(net_feed_input) == 1) sess = rt.InferenceSession(output_file) onnx_result = sess.run( - None, {net_feed_input[0]: img_list[0].detach().numpy()})[0] - if not np.allclose(pytorch_result, onnx_result): - raise ValueError( - 'The outputs are different between Pytorch and ONNX') + None, {net_feed_input[0]: img_list[0].detach().numpy()})[0][0] + # show segmentation results + if show: + import cv2 + import os.path as osp + img = img_meta_list[0][0]['filename'] + if not osp.exists(img): + img = imgs[0][:3, ...].permute(1, 2, 0) * 255 + img = img.detach().numpy().astype(np.uint8) + # resize onnx_result to ori_shape + onnx_result_ = cv2.resize(onnx_result[0].astype(np.uint8), + (ori_shape[1], ori_shape[0])) + show_result_pyplot( + model, + img, (onnx_result_, ), + palette=model.PALETTE, + block=False, + title='ONNXRuntime', + opacity=0.5) + + # resize pytorch_result to ori_shape + pytorch_result_ = cv2.resize(pytorch_result[0].astype(np.uint8), + (ori_shape[1], ori_shape[0])) + show_result_pyplot( + model, + img, (pytorch_result_, ), + title='PyTorch', + palette=model.PALETTE, + opacity=0.5) + # compare results + np.testing.assert_allclose( + pytorch_result.astype(np.float32) / num_classes, + onnx_result.astype(np.float32) / num_classes, + rtol=1e-5, + atol=1e-5, + err_msg='The outputs are different between Pytorch and ONNX') print('The outputs are same between Pytorch and ONNX') @@ -149,7 +257,12 @@ def parse_args(): parser = argparse.ArgumentParser(description='Convert MMSeg to ONNX') parser.add_argument('config', help='test config file path') parser.add_argument('--checkpoint', help='checkpoint file', default=None) - parser.add_argument('--show', action='store_true', help='show onnx graph') + parser.add_argument( + '--input-img', type=str, help='Images for input', default=None) + parser.add_argument( + '--show', + action='store_true', + help='show onnx graph and segmentation results') parser.add_argument( '--verify', action='store_true', help='verify the onnx model') parser.add_argument('--output-file', type=str, default='tmp.onnx') @@ -160,6 +273,20 @@ def parse_args(): nargs='+', default=[256, 256], help='input image size') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='Override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + parser.add_argument( + '--dynamic-export', + action='store_true', + help='Whether to export onnx with dynamic axis.') args = parser.parse_args() return args @@ -178,6 +305,8 @@ def parse_args(): raise ValueError('invalid input shape') cfg = mmcv.Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) cfg.model.pretrained = None # build the model and load checkpoint @@ -188,13 +317,28 @@ def parse_args(): segmentor = _convert_batchnorm(segmentor) if args.checkpoint: - load_checkpoint(segmentor, args.checkpoint, map_location='cpu') + checkpoint = load_checkpoint( + segmentor, args.checkpoint, map_location='cpu') + segmentor.CLASSES = checkpoint['meta']['CLASSES'] + segmentor.PALETTE = checkpoint['meta']['PALETTE'] + + # read input or create dummpy input + if args.input_img is not None: + mm_inputs = _prepare_input_img(args.input_img, cfg.data.test.pipeline, + (input_shape[3], input_shape[2])) + else: + if isinstance(segmentor.decode_head, nn.ModuleList): + num_classes = segmentor.decode_head[-1].num_classes + else: + num_classes = segmentor.decode_head.num_classes + mm_inputs = _demo_mm_inputs(input_shape, num_classes) - # conver model to onnx file + # convert model to onnx file pytorch2onnx( segmentor, - input_shape, + mm_inputs, opset_version=args.opset_version, show=args.show, output_file=args.output_file, - verify=args.verify) + verify=args.verify, + dynamic_export=args.dynamic_export) diff --git a/tools/pytorch2torchscript.py b/tools/pytorch2torchscript.py new file mode 100644 index 00000000000..206c4bb457e --- /dev/null +++ b/tools/pytorch2torchscript.py @@ -0,0 +1,184 @@ +import argparse + +import mmcv +import numpy as np +import torch +import torch._C +import torch.serialization +from mmcv.runner import load_checkpoint +from torch import nn + +from mmseg.models import build_segmentor + +torch.manual_seed(3) + + +def digit_version(version_str): + digit_version = [] + for x in version_str.split('.'): + if x.isdigit(): + digit_version.append(int(x)) + elif x.find('rc') != -1: + patch_version = x.split('rc') + digit_version.append(int(patch_version[0]) - 1) + digit_version.append(int(patch_version[1])) + return digit_version + + +def check_torch_version(): + torch_minimum_version = '1.8.0' + torch_version = digit_version(torch.__version__) + + assert (torch_version >= digit_version(torch_minimum_version)), \ + f'Torch=={torch.__version__} is not support for converting to ' \ + f'torchscript. Please install pytorch>={torch_minimum_version}.' + + +def _convert_batchnorm(module): + module_output = module + if isinstance(module, torch.nn.SyncBatchNorm): + module_output = torch.nn.BatchNorm2d(module.num_features, module.eps, + module.momentum, module.affine, + module.track_running_stats) + if module.affine: + module_output.weight.data = module.weight.data.clone().detach() + module_output.bias.data = module.bias.data.clone().detach() + # keep requires_grad unchanged + module_output.weight.requires_grad = module.weight.requires_grad + module_output.bias.requires_grad = module.bias.requires_grad + module_output.running_mean = module.running_mean + module_output.running_var = module.running_var + module_output.num_batches_tracked = module.num_batches_tracked + for name, child in module.named_children(): + module_output.add_module(name, _convert_batchnorm(child)) + del module + return module_output + + +def _demo_mm_inputs(input_shape, num_classes): + """Create a superset of inputs needed to run test or train batches. + + Args: + input_shape (tuple): + input batch dimensions + num_classes (int): + number of semantic classes + """ + (N, C, H, W) = input_shape + rng = np.random.RandomState(0) + imgs = rng.rand(*input_shape) + segs = rng.randint( + low=0, high=num_classes - 1, size=(N, 1, H, W)).astype(np.uint8) + img_metas = [{ + 'img_shape': (H, W, C), + 'ori_shape': (H, W, C), + 'pad_shape': (H, W, C), + 'filename': '.png', + 'scale_factor': 1.0, + 'flip': False, + } for _ in range(N)] + mm_inputs = { + 'imgs': torch.FloatTensor(imgs).requires_grad_(True), + 'img_metas': img_metas, + 'gt_semantic_seg': torch.LongTensor(segs) + } + return mm_inputs + + +def pytorch2libtorch(model, + input_shape, + show=False, + output_file='tmp.pt', + verify=False): + """Export Pytorch model to TorchScript model and verify the outputs are + same between Pytorch and TorchScript. + + Args: + model (nn.Module): Pytorch model we want to export. + input_shape (tuple): Use this input shape to construct + the corresponding dummy input and execute the model. + show (bool): Whether print the computation graph. Default: False. + output_file (string): The path to where we store the + output TorchScript model. Default: `tmp.pt`. + verify (bool): Whether compare the outputs between + Pytorch and TorchScript. Default: False. + """ + if isinstance(model.decode_head, nn.ModuleList): + num_classes = model.decode_head[-1].num_classes + else: + num_classes = model.decode_head.num_classes + + mm_inputs = _demo_mm_inputs(input_shape, num_classes) + + imgs = mm_inputs.pop('imgs') + + # replace the orginal forword with forward_dummy + model.forward = model.forward_dummy + model.eval() + traced_model = torch.jit.trace( + model, + example_inputs=imgs, + check_trace=verify, + ) + + if show: + print(traced_model.graph) + + traced_model.save(output_file) + print('Successfully exported TorchScript model: {}'.format(output_file)) + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Convert MMSeg to TorchScript') + parser.add_argument('config', help='test config file path') + parser.add_argument('--checkpoint', help='checkpoint file', default=None) + parser.add_argument( + '--show', action='store_true', help='show TorchScript graph') + parser.add_argument( + '--verify', action='store_true', help='verify the TorchScript model') + parser.add_argument('--output-file', type=str, default='tmp.pt') + parser.add_argument( + '--shape', + type=int, + nargs='+', + default=[512, 512], + help='input image size (height, width)') + args = parser.parse_args() + return args + + +if __name__ == '__main__': + args = parse_args() + check_torch_version() + + if len(args.shape) == 1: + input_shape = (1, 3, args.shape[0], args.shape[0]) + elif len(args.shape) == 2: + input_shape = ( + 1, + 3, + ) + tuple(args.shape) + else: + raise ValueError('invalid input shape') + + cfg = mmcv.Config.fromfile(args.config) + cfg.model.pretrained = None + + # build the model and load checkpoint + cfg.model.train_cfg = None + segmentor = build_segmentor( + cfg.model, train_cfg=None, test_cfg=cfg.get('test_cfg')) + # convert SyncBN to BN + segmentor = _convert_batchnorm(segmentor) + + if args.checkpoint: + load_checkpoint(segmentor, args.checkpoint, map_location='cpu') + + # convert the PyTorch model to LibTorch model + pytorch2libtorch( + segmentor, + input_shape, + show=args.show, + output_file=args.output_file, + verify=args.verify) diff --git a/tools/test.py b/tools/test.py index 3ed22efa653..c074fcc4bbe 100644 --- a/tools/test.py +++ b/tools/test.py @@ -17,11 +17,6 @@ def parse_args(): description='mmseg test (and eval) a model') parser.add_argument('config', help='test config file path') parser.add_argument('checkpoint', help='checkpoint file') - parser.add_argument( - '-p', - '--port', - default='25900', - help='The data transmit port when distributed training.') parser.add_argument( '--aug-test', action='store_true', help='Use Flip and Multi scale aug') parser.add_argument('--out', help='output result file in pickle format') @@ -90,9 +85,6 @@ def main(): cfg = mmcv.Config.fromfile(args.config) if args.options is not None: cfg.merge_from_dict(args.options) - # set transmit port - if args.port != '25900': - cfg.dist_params['port'] = args.port # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True diff --git a/tools/train.py b/tools/train.py index a8f9fbd4729..51fe4065de1 100644 --- a/tools/train.py +++ b/tools/train.py @@ -28,11 +28,6 @@ def parse_args(): '--no-validate', action='store_true', help='whether not to evaluate the checkpoint during training') - parser.add_argument( - '-p', - '--port', - default='25900', - help='The data transmit port when distributed training.') group_gpus = parser.add_mutually_exclusive_group() group_gpus.add_argument( '--gpus', @@ -71,9 +66,6 @@ def main(): cfg = Config.fromfile(args.config) if args.options is not None: cfg.merge_from_dict(args.options) - # set transmit port - if args.port != '25900': - cfg.dist_params['port'] = args.port # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True