Models:
  - Name: resnet50
    Results:
      - Dataset: ImageNet-1k
        Metrics:
          Top 1 Accuracy: 76.55
          Top 5 Accuracy: 93.06
    Config: configs/resnet/resnet50_8xb32_in1k.py
    Gpus: 8

  - Name: seresnet50
    Results:
      - Dataset: ImageNet-1k
        Metrics:
          Top 1 Accuracy: 77.74
          Top 5 Accuracy: 93.84
    Config: configs/seresnet/seresnet50_8xb32_in1k.py
    Gpus: 8

  - Name: vit-base
    Results:
      - Dataset: ImageNet-1k
        Metrics:
          Top 1 Accuracy: 82.37
          Top 5 Accuracy: 96.15
    Config: configs/vision_transformer/vit-base-p16_pt-32xb128-mae_in1k-224.py
    Gpus: 32

  - Name: mobilenetv2
    Results:
      - Dataset: ImageNet-1k
        Metrics:
          Top 1 Accuracy: 71.86
          Top 5 Accuracy: 90.42
    Config: configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py
    Gpus: 8

  - 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

  - Name: vgg16
    Results:
      - Dataset: ImageNet-1k
        Metrics:
          Top 1 Accuracy: 71.62
          Top 5 Accuracy: 90.49
    Config: configs/vgg/vgg16_8xb32_in1k.py
    Gpus: 8
    Months:
      - 1
      - 4
      - 7
      - 10

  - Name: shufflenet_v2
    Results:
      - Dataset: ImageNet-1k
        Metrics:
          Top 1 Accuracy: 69.55
          Top 5 Accuracy: 88.92
    Config: configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py
    Gpus: 16
    Months:
      - 2
      - 5
      - 8
      - 11

  - Name: resnet-rsb
    Results:
      - Dataset: ImageNet-1k
        Metrics:
          Top 1 Accuracy: 80.12
          Top 5 Accuracy: 94.78
    Config: configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py
    Gpus: 8
    Months:
      - 3
      - 6
      - 9
      - 12
