bench_train.yml

Models:
  - Name: resnet34
    Results:
      - Dataset: ImageNet-1k
        Metrics:
          Top 1 Accuracy: 73.85
          Top 5 Accuracy: 91.53
    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.pth
    Config: configs/resnet/resnet34_8xb32_in1k.py
    Gpus: 8

  - Name: vgg11bn
    Results:
      - Dataset: ImageNet-1k
        Metrics:
          Top 1 Accuracy: 70.75
          Top 5 Accuracy: 90.12
    Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth
    Config: configs/vgg/vgg11bn_8xb32_in1k.py
    Gpus: 8

  - Name: seresnet50
    Results:
      - Dataset: ImageNet-1k
        Metrics:
          Top 1 Accuracy: 77.74
          Top 5 Accuracy: 93.84
    Weights: https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth
    Config: configs/seresnet/seresnet50_8xb32_in1k.py
    Gpus: 8

  - Name: resnext50
    Results:
      - Dataset: ImageNet-1k
        Metrics:
          Top 1 Accuracy: 77.92
          Top 5 Accuracy: 93.74
    Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_batch256_imagenet_20200708-c07adbb7.pth
    Config: configs/resnext/resnext50-32x4d_8xb32_in1k.py
    Gpus: 8

  - Name: mobilenet
    Results:
      - Dataset: ImageNet-1k
        Metrics:
          Top 1 Accuracy: 71.86
          Top 5 Accuracy: 90.42
    Weights: https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth
    Config: configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py
    Gpus: 8
    Months:
      - 1
      - 4
      - 7
      - 10

  - Name: shufflenet_v1
    Results:
      - Dataset: ImageNet-1k
        Metrics:
          Top 1 Accuracy: 68.13
          Top 5 Accuracy: 87.81
    Weights: https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.pth
    Config: configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py
    Gpus: 16
    Months:
      - 2
      - 5
      - 8
      - 11

  - Name: swin_tiny
    Results:
    - Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 81.18
        Top 5 Accuracy: 95.61
    Weights: https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth
    Config: configs/swin_transformer/swin-tiny_16xb64_in1k.py
    Gpus: 16
    Months:
      - 3
      - 6
      - 9
      - 12