Collections:
  - Name: MoCoV3
    Metadata:
      Training Data: ImageNet-1k
      Training Techniques:
        - LARS
      Training Resources: 32x V100 GPUs
      Architecture:
        - ResNet
        - ViT
        - MoCo
    Paper:
        URL: https://arxiv.org/abs/2104.02057
        Title: "An Empirical Study of Training Self-Supervised Vision Transformers"
    README: configs/selfsup/mocov3/README.md

Models:
  - Name: mocov3_resnet50_8xb512-amp-coslr-100e_in1k
    In Collection: MoCoV3
    Metadata:
      Epochs: 100
      Batch Size: 4096
    Results: null
    Config: configs/selfsup/mocov3/mocov3_resnet50_8xb512-amp-coslr-100e_in1k.py
    Weights: https://download.openmmlab.com/mmselfsup/1.x/mocov3/mocov3_resnet50_8xb512-amp-coslr-100e_in1k/mocov3_resnet50_8xb512-amp-coslr-100e_in1k_20220927-f1144efa.pth
    Downstream:
      - Type: Image Classification
        Metadata:
          Epochs: 90
          Batch Size: 1024
        Results:
          - Task: Linear Evaluation
            Dataset: ImageNet-1k
            Metrics:
              Top 1 Accuracy: 69.6
        Config: configs/benchmarks/classification/imagenet/resnet50_linear-8xb128-coslr-90e_in1k.py
        Weights: https://download.openmmlab.com/mmselfsup/1.x/mocov3/mocov3_resnet50_8xb512-amp-coslr-100e_in1k/resnet50_linear-8xb128-coslr-90e_in1k/resnet50_linear-8xb128-coslr-90e_in1k_20220927-8f7d937e.pth
  - Name: mocov3_resnet50_8xb512-amp-coslr-100e_in1k
    In Collection: MoCoV3
    Metadata:
      Epochs: 300
      Batch Size: 4096
    Results: null
    Config: configs/selfsup/mocov3/mocov3_resnet50_8xb512-amp-coslr-300e_in1k.py
    Weights: https://download.openmmlab.com/mmselfsup/1.x/mocov3/mocov3_resnet50_8xb512-amp-coslr-300e_in1k/mocov3_resnet50_8xb512-amp-coslr-300e_in1k_20220927-1e4f3304.pth
    Downstream:
      - Type: Image Classification
        Metadata:
          Epochs: 90
          Batch Size: 1024
        Results:
          - Task: Linear Evaluation
            Dataset: ImageNet-1k
            Metrics:
              Top 1 Accuracy: 72.8
        Config: configs/benchmarks/classification/imagenet/resnet50_linear-8xb128-coslr-90e_in1k.py
        Weights: https://download.openmmlab.com/mmselfsup/1.x/mocov3/mocov3_resnet50_8xb512-amp-coslr-300e_in1k/resnet50_linear-8xb128-coslr-90e_in1k/resnet50_linear-8xb128-coslr-90e_in1k_20220927-d21ddac2.pth
  - Name: mocov3_resnet50_8xb512-amp-coslr-100e_in1k
    In Collection: MoCoV3
    Metadata:
      Epochs: 800
      Batch Size: 4096
    Results: null
    Config: configs/selfsup/mocov3/mocov3_resnet50_8xb512-amp-coslr-300e_in1k.py
    Weights: https://download.openmmlab.com/mmselfsup/1.x/mocov3/mocov3_resnet50_8xb512-amp-coslr-800e_in1k/mocov3_resnet50_8xb512-amp-coslr-800e_in1k_20220927-e043f51a.pth
    Downstream:
      - Type: Image Classification
        Metadata:
          Epochs: 90
          Batch Size: 1024
        Results:
          - Task: Linear Evaluation
            Dataset: ImageNet-1k
            Metrics:
              Top 1 Accuracy: 74.4
        Config: configs/benchmarks/classification/imagenet/resnet50_linear-8xb128-coslr-90e_in1k.py
        Weights: https://download.openmmlab.com/mmselfsup/1.x/mocov3/mocov3_resnet50_8xb512-amp-coslr-800e_in1k/resnet50_linear-8xb128-coslr-90e_in1k/resnet50_linear-8xb128-coslr-90e_in1k_20220927-0e97a483.pth
  - Name: mocov3_vit-small-p16_16xb256-amp-coslr-300e_in1k
    In Collection: MoCoV3
    Metadata:
      Epochs: 300
      Batch Size: 4096
    Results: null
    Config: configs/selfsup/mocov3/mocov3_vit-small-p16_16xb256-amp-coslr-300e_in1k.py
    Weights: https://download.openmmlab.com/mmselfsup/1.x/mocov3/mocov3_vit-small-p16_16xb256-amp-coslr-300e_in1k/mocov3_vit-small-p16_16xb256-amp-coslr-300e_in1k-224_20220826-08bc52f7.pth
    Downstream:
      - Type: Image Classification
        Metadata:
          Epochs: 90
          Batch Size: 1024
        Results:
          - Task: Linear Evaluation
            Dataset: ImageNet-1k
            Metrics:
              Top 1 Accuracy: 73.6
        Config: configs/benchmarks/classification/imagenet/vit-small-p16_linear-8xb128-coslr-90e_in1k.py
        Weights: https://download.openmmlab.com/mmselfsup/1.x/mocov3/mocov3_vit-small-p16_16xb256-amp-coslr-300e_in1k/vit-small-p16_linear-8xb128-coslr-90e_in1k/vit-small-p16_linear-8xb128-coslr-90e_in1k_20220826-376674ef.pth
  - Name: mocov3_vit-base-p16_16xb256-amp-coslr-300e_in1k
    In Collection: MoCoV3
    Metadata:
      Epochs: 300
      Batch Size: 4096
    Results: null
    Config: configs/selfsup/mocov3/mocov3_vit-base-p16_16xb256-amp-coslr-300e_in1k.py
    Weights: https://download.openmmlab.com/mmselfsup/1.x/mocov3/mocov3_vit-base-p16_16xb256-amp-coslr-300e_in1k/mocov3_vit-base-p16_16xb256-amp-coslr-300e_in1k-224_20220826-25213343.pth
    Downstream:
      - Type: Image Classification
        Metadata:
          Epochs: 90
          Batch Size: 1024
        Results:
          - Task: Linear Evaluation
            Dataset: ImageNet-1k
            Metrics:
              Top 1 Accuracy: 76.9
        Config: configs/benchmarks/classification/imagenet/vit-base-p16_linear-8xb128-coslr-90e_in1k.py
        Weights: https://download.openmmlab.com/mmselfsup/1.x/mocov3/mocov3_vit-base-p16_16xb256-amp-coslr-300e_in1k/vit-base-p16_linear-8xb128-coslr-90e_in1k/vit-base-p16_linear-8xb128-coslr-90e_in1k_20220826-83be7758.pth
      - Type: Image Classification
        Metadata:
          Epochs: 150
          Batch Size: 512
        Results:
          - Task: Fine-tuning
            Dataset: ImageNet-1k
            Metrics:
              Top 1 Accuracy: 83.0
        Config: configs/benchmarks/classification/imagenet/vit-base-p16_ft-8xb64-coslr-150e_in1k.py
        Weights: https://download.openmmlab.com/mmselfsup/1.x/mocov3/mocov3_vit-base-p16_16xb256-amp-coslr-300e_in1k/vit-base-p16_ft-8xb64-coslr-150e_in1k/vit-base-p16_ft-8xb64-coslr-150e_in1k_20220826-f1e6c442.pth
  - Name: mocov3_vit-large-p16_64xb64-amp-coslr-300e_in1k
    In Collection: MoCoV3
    Metadata:
      Epochs: 300
      Batch Size: 4096
    Results: null
    Config: configs/selfsup/mocov3/mocov3_vit-base-p16_64xb64-amp-coslr-300e_in1k.py
    Weights: https://download.openmmlab.com/mmselfsup/1.x/mocov3/mocov3_vit-large-p16_64xb64-amp-coslr-300e_in1k/mocov3_vit-large-p16_64xb64-amp-coslr-300e_in1k-224_20220829-9b88a442.pth
    Downstream:
      - Type: Image Classification
        Metadata:
          Epochs: 100
          Batch Size: 512
        Results:
          - Task: Fine-tuning
            Dataset: ImageNet-1k
            Metrics:
              Top 1 Accuracy: 83.7
        Config: configs/benchmarks/classification/imagenet/vit-large-p16_ft-8xb64-coslr-150e_in1k.py
        Weights: https://download.openmmlab.com/mmselfsup/1.x/mocov3/mocov3_vit-large-p16_64xb64-amp-coslr-300e_in1k/vit-large-p16_ft-8xb64-coslr-100e_in1k/vit-large-p16_ft-8xb64-coslr-100e_in1k_20220829-878a2f7f.pth
