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Refactor YOLOX #6443

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Nov 24, 2021
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8e297ff
Fix aug test error when the number of prediction bboxes is 0 (#6398)
hhaAndroid Nov 2, 2021
892f910
Support custom pin_memory and persistent_workers
hhaAndroid Nov 3, 2021
0f1a529
[Docs] Chinese version of robustness_benchmarking.md (#6375)
chhluo Nov 3, 2021
b627391
update yolox_s
hhaAndroid Nov 5, 2021
7b5710d
update yolox_s
hhaAndroid Nov 5, 2021
32badc6
support dynamic eval interval
hhaAndroid Nov 5, 2021
090258f
Merge branch 'support_pinmemory_persistent_workers' of github.com:hha…
hhaAndroid Nov 5, 2021
b23fa00
fix some error
hhaAndroid Nov 5, 2021
9ead700
support ceph
hhaAndroid Nov 5, 2021
72f2035
fix none error
hhaAndroid Nov 5, 2021
edfa292
fix batch error
hhaAndroid Nov 5, 2021
c7bf4e8
replace resize
hhaAndroid Nov 6, 2021
4db90f3
fix comment
hhaAndroid Nov 8, 2021
6082efe
fix docstr
hhaAndroid Nov 8, 2021
ec304c6
Update the link of checkpoints (#6460)
AronLin Nov 8, 2021
e1bf88b
[Feature]: Support plot confusion matrix. (#6344)
RangiLyu Nov 9, 2021
5694392
remove pin_memory
hhaAndroid Nov 9, 2021
ce754da
update
hhaAndroid Nov 9, 2021
9a00d51
Merge branch 'support_pinmemory_persistent_workers' of github.com:hha…
hhaAndroid Nov 9, 2021
af1da0d
fix unittest
hhaAndroid Nov 9, 2021
84efef4
update cfg
hhaAndroid Nov 10, 2021
e4f3643
fix error
hhaAndroid Nov 10, 2021
3ab708d
add unittest
hhaAndroid Nov 10, 2021
59e86fd
[Fix] Fix SpatialReductionAttention in PVT. (#6488)
RangiLyu Nov 15, 2021
0f490a1
Save coco summarize print information to logger (#6505)
hhaAndroid Nov 15, 2021
be174ab
Fix type error in 2_new_data_mode (#6469)
AndreaPi Nov 15, 2021
39838c2
bump version to v2.18.1 (#6510)
RangiLyu Nov 15, 2021
fdbc678
add some comment
hhaAndroid Nov 17, 2021
85630b1
Merge branch 'dev-v2.18.1' of https://github.com/open-mmlab/mmdetecti…
hhaAndroid Nov 17, 2021
849b3cc
fix some comment
hhaAndroid Nov 17, 2021
5edbf07
update readme
hhaAndroid Nov 17, 2021
7fb81cd
fix lint
hhaAndroid Nov 17, 2021
9d1f925
Merge branch 'dev-v2.19.0' of https://github.com/open-mmlab/mmdetecti…
hhaAndroid Nov 17, 2021
0e381a7
add reduce mean
hhaAndroid Nov 23, 2021
58bdcb0
update
hhaAndroid Nov 23, 2021
e4b8b8f
update readme
hhaAndroid Nov 23, 2021
0d7c7c9
merge
hhaAndroid Nov 23, 2021
72dcbba
update params
hhaAndroid Nov 23, 2021
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16 changes: 8 additions & 8 deletions README.md
Original file line number Diff line number Diff line change
@@ -13,10 +13,10 @@
<img src="https://user-images.githubusercontent.com/12907710/137271636-56ba1cd2-b110-4812-8221-b4c120320aa9.png"/>


[📘Documentation](https://mmdetection.readthedocs.io/en/v2.18.0/) |
[🛠️Installation](https://mmdetection.readthedocs.io/en/v2.18.0/get_started.html) |
[👀Model Zoo](https://mmdetection.readthedocs.io/zh_CN/v2.18.0/model_zoo.html) |
[🆕Update News](https://mmdetection.readthedocs.io/en/v2.18.0/changelog.html) |
[📘Documentation](https://mmdetection.readthedocs.io/en/v2.18.1/) |
[🛠️Installation](https://mmdetection.readthedocs.io/en/v2.18.1/get_started.html) |
[👀Model Zoo](https://mmdetection.readthedocs.io/zh_CN/v2.18.1/model_zoo.html) |
[🆕Update News](https://mmdetection.readthedocs.io/en/v2.18.1/changelog.html) |
[🚀Ongoing Projects](https://github.com/open-mmlab/mmdetection/projects) |
[🤔Reporting Issues](https://github.com/open-mmlab/mmdetection/issues/new/choose)

@@ -60,10 +60,10 @@ This project is released under the [Apache 2.0 license](LICENSE).

## Changelog

**2.18.0** was released in 27/10/2021:
- Support [QueryInst](http://arxiv.org/abs/2105.01928).
- Refactor dense_head to decouple onnx export from get_bboxes and speed up inference time.
- Support infinite sampler to accelerate training when using iter based runner.
**2.18.1** was released in 15/11/2021:
- Release [QueryInst](http://arxiv.org/abs/2105.01928) pre-trained weights.
- Support plot confusion matrix.
- Fix SpatialReductionAttention in PVT and fix trunc_normal_init in both PVT and Swin-Transformer.

Please refer to [changelog.md](docs/changelog.md) for details and release history.

16 changes: 8 additions & 8 deletions README_zh-CN.md
Original file line number Diff line number Diff line change
@@ -13,10 +13,10 @@
<img src="https://user-images.githubusercontent.com/12907710/137271636-56ba1cd2-b110-4812-8221-b4c120320aa9.png"/>


[📘使用文档](https://mmdetection.readthedocs.io/zh_CN/v2.18.0/) |
[🛠️安装教程](https://mmdetection.readthedocs.io/zh_CN/v2.18.0/get_started.html) |
[👀模型库](https://mmdetection.readthedocs.io/zh_CN/v2.18.0/model_zoo.html) |
[🆕更新日志](https://mmdetection.readthedocs.io/en/v2.18.0/changelog.html) |
[📘使用文档](https://mmdetection.readthedocs.io/zh_CN/v2.18.1/) |
[🛠️安装教程](https://mmdetection.readthedocs.io/zh_CN/v2.18.1/get_started.html) |
[👀模型库](https://mmdetection.readthedocs.io/zh_CN/v2.18.1/model_zoo.html) |
[🆕更新日志](https://mmdetection.readthedocs.io/en/v2.18.1/changelog.html) |
[🚀进行中的项目](https://github.com/open-mmlab/mmdetection/projects) |
[🤔报告问题](https://github.com/open-mmlab/mmdetection/issues/new/choose)

@@ -59,10 +59,10 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope

## 更新日志

最新的 **2.18.0** 版本已经在2021.10.27发布:
- 支持 [QueryInst](http://arxiv.org/abs/2105.01928)。
- 重构 dense_head 以将 onnx 导出与 get_bboxes 解耦并加快推理时间
- 支持 infinite sampler 加速 iter based runner 训练
最新的 **2.18.1** 版本已经在2021.11.15发布:
- 发布了 [QueryInst](http://arxiv.org/abs/2105.01928) 的预训练模型
- 支持绘制混淆矩阵
- 修复了 PVT 中的 SpatialReductionAttention 以及 PVT 和 Swin-Transformer 中 trunc_normal_init 的错误用法

如果想了解更多版本更新细节和历史信息,请阅读[更新日志](docs/changelog.md)。

21 changes: 9 additions & 12 deletions configs/groie/README.md
Original file line number Diff line number Diff line change
@@ -25,9 +25,6 @@ performance.
## Results and models

The results on COCO 2017 minival (5k images) are shown in the below table.
You can find
[here](https://drive.google.com/drive/folders/19ssstbq_h0Z1cgxHmJYFO8s1arf3QJbT)
the trained models.

### Application of GRoIE to different architectures

@@ -42,24 +39,24 @@ the trained models.
| R-50-FPN | GC-Net | 1x | 40.7 | 36.5 | [config](../gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202_085547.log.json) |
| R-50-FPN | + GRoIE | 1x | 41.0 | 37.8 | [config](./mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth) |
| R-101-FPN | GC-Net | 1x | 42.2 | 37.8 | [config](../gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206_142508.log.json) |
| R-101-FPN | + GRoIE | 1x | | | [config](./mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py)| [model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507-8daae01c.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507.log.json) |
| R-101-FPN | + GRoIE | 1x | 42.6 | 38.7 | [config](./mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py)| [model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507-8daae01c.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507.log.json) |

## Citation

If you use this work or benchmark in your research, please cite this project.

```latex
@misc{rossi2020novel,
title={A novel Region of Interest Extraction Layer for Instance Segmentation},
author={Leonardo Rossi and Akbar Karimi and Andrea Prati},
year={2020},
eprint={2004.13665},
archivePrefix={arXiv},
primaryClass={cs.CV}
@inproceedings{rossi2021novel,
title={A novel region of interest extraction layer for instance segmentation},
author={Rossi, Leonardo and Karimi, Akbar and Prati, Andrea},
booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},
pages={2203--2209},
year={2021},
organization={IEEE}
}
```

## Contact

The implementation of GROI is currently maintained by
The implementation of GRoIE is currently maintained by
[Leonardo Rossi](https://github.com/hachreak/).
10 changes: 5 additions & 5 deletions configs/queryinst/README.md
Original file line number Diff line number Diff line change
@@ -19,8 +19,8 @@

| Model | Backbone | Style | Lr schd | Number of Proposals |Multi-Scale| RandomCrop | box AP | mask AP | Config | Download |
|:------------:|:---------:|:-------:|:-------:|:-------: |:-------: |:---------:|:------:|:------:|:------:|:--------:|
| QueryInst | R-50-FPN | pytorch | 1x | 100 | False | False | 42.0 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r50_fpn_1x_coco.py) | [model]() &#124; [log]() |
| QueryInst | R-50-FPN | pytorch | 3x | 100 | True | False | 44.8 | 39.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco.py) | [model]() &#124; [log]() |
| QueryInst | R-50-FPN | pytorch | 3x | 300 | True | True | 47.5 | 41.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py) | [model]() &#124; [log]() |
| QueryInst | R-101-FPN | pytorch | 3x | 100 | True | False | 46.4 | 41.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco.py) | [model]() &#124; [log]() |
| QueryInst | R-101-FPN | pytorch | 3x | 300 | True | True | 49.0 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py) | [model]() &#124; [log]() |
| QueryInst | R-50-FPN | pytorch | 1x | 100 | False | False | 42.0 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_1x_coco/queryinst_r50_fpn_1x_coco_20210907_084916-5a8f1998.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_1x_coco/queryinst_r50_fpn_1x_coco_20210907_084916.log.json) |
| QueryInst | R-50-FPN | pytorch | 3x | 100 | True | False | 44.8 | 39.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco/queryinst_r50_fpn_mstrain_480-800_3x_coco_20210901_103643-7837af86.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco/queryinst_r50_fpn_mstrain_480-800_3x_coco_20210901_103643.log.json) |
| QueryInst | R-50-FPN | pytorch | 3x | 300 | True | True | 47.5 | 41.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_101802-85cffbd8.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_101802.log.json) |
| QueryInst | R-101-FPN | pytorch | 3x | 100 | True | False | 46.4 | 41.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco/queryinst_r101_fpn_mstrain_480-800_3x_coco_20210904_104048-91f9995b.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco/queryinst_r101_fpn_mstrain_480-800_3x_coco_20210904_104048.log.json) |
| QueryInst | R-101-FPN | pytorch | 3x | 300 | True | True | 49.0 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_153621-76cce59f.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_153621.log.json) |
10 changes: 5 additions & 5 deletions configs/queryinst/metafile.yml
Original file line number Diff line number Diff line change
@@ -33,7 +33,7 @@ Models:
Dataset: COCO
Metrics:
mask AP: 37.5
Weights:
Weights: https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_1x_coco/queryinst_r50_fpn_1x_coco_20210907_084916-5a8f1998.pth

- Name: queryinst_r50_fpn_mstrain_480-800_3x_coco
In Collection: QueryInst
@@ -49,7 +49,7 @@ Models:
Dataset: COCO
Metrics:
mask AP: 39.8
Weights:
Weights: https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco/queryinst_r50_fpn_mstrain_480-800_3x_coco_20210901_103643-7837af86.pth

- Name: queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco
In Collection: QueryInst
@@ -65,7 +65,7 @@ Models:
Dataset: COCO
Metrics:
mask AP: 41.7
Weights:
Weights: https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_101802-85cffbd8.pth

- Name: queryinst_r101_fpn_mstrain_480-800_3x_coco
In Collection: QueryInst
@@ -81,7 +81,7 @@ Models:
Dataset: COCO
Metrics:
mask AP: 41.0
Weights:
Weights: https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco/queryinst_r101_fpn_mstrain_480-800_3x_coco_20210904_104048-91f9995b.pth

- Name: queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco
In Collection: QueryInst
@@ -97,4 +97,4 @@ Models:
Dataset: COCO
Metrics:
mask AP: 42.9
Weights:
Weights: https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_153621-76cce59f.pth
6 changes: 5 additions & 1 deletion configs/yolox/README.md
Original file line number Diff line number Diff line change
@@ -17,9 +17,13 @@

| Backbone | size | Mem (GB) | box AP | Config | Download |
|:---------:|:-------:|:-------:|:-------:|:--------:|:------:|
| YOLOX-s | 640 | 7.9 | 40.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox/yolox_s_8x8_300e_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_s_8x8_300e_coco/yolox_s_8x8_300e_coco_20211110_150543-699b132f.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_s_8x8_300e_coco/yolox_s_8x8_300e_coco_20211110_150543.log.json) |
| YOLOX-l | 640 | 19.6 | 49.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox/yolox_l_8x8_300e_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211113_121356-5e33d212.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211113_121356.log.json) |
| YOLOX-Tiny | 416 | 3.6 | 31.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox/yolox_tiny_8x8_300e_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_20210806_234250-4ff3b67e.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_20210806_234250.log.json) |


**Note**:

1. The test score threshold is 0.001.
2. We find that the performance is unstable and may fluctuate by about 0.7 mAP. We will continue to investigate and improve it.
2. YOLOX-s and YOLOX-l are the models corresponding to the best map on the validation set.
3. We find that the performance is unstable and may fluctuate by about 0.7 mAP. We will continue to investigate and improve it.
25 changes: 25 additions & 0 deletions configs/yolox/metafile.yml
Original file line number Diff line number Diff line change
@@ -18,7 +18,32 @@ Collections:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.15.1/mmdet/models/detectors/yolox.py#L6
Version: v2.15.1


Models:
- Name: yolox_s_8x8_300e_coco
In Collection: YOLOX
Config: configs/yolox/yolox_s_8x8_300e_coco.py
Metadata:
Training Memory (GB): 7.9
Epochs: 295
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 40.2
Weights: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_s_8x8_300e_coco/yolox_s_8x8_300e_coco_20211110_150543-699b132f.pth
- Name: yolox_l_8x8_300e_coco
In Collection: YOLOX
Config: configs/yolox/yolox_l_8x8_300e_coco.py
Metadata:
Training Memory (GB): 17.9
Epochs: 292
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 49.4
Weights: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211113_121356-5e33d212.pth
- Name: yolox_tiny_8x8_300e_coco
In Collection: YOLOX
Config: configs/yolox/yolox_tiny_8x8_300e_coco.py
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