Collections:
  - Name: SimCLR
    Metadata:
      Training Data: ImageNet-1k
      Training Techniques:
        - LARS
      Training Resources: 8x V100 GPUs (b256), 16x A100-80G GPUs (b4096)
      Architecture:
        - ResNet
        - SimCLR
    Paper:
        URL: https://arxiv.org/abs/2002.05709
        Title: "A simple framework for contrastive learning of visual representations"
    README: configs/selfsup/simclr/README.md

Models:
  - Name: simclr_resnet50_8xb32-coslr-200e_in1k
    In Collection: SimCLR
    Metadata:
      Epochs: 200
      Batch Size: 256
    Results: null
    Config: configs/selfsup/simclr/simclr_resnet50_8xb32-coslr-200e_in1k.py
    Weights: https://download.openmmlab.com/mmselfsup/1.x/simclr/simclr_resnet50_8xb32-coslr-200e_in1k/simclr_resnet50_8xb32-coslr-200e_in1k_20220825-15f807a4.pth
    Downstream:
      - Type: Image Classification
        Metadata:
          Epochs: 90
          Batch Size: 4096
        Results:
          - Task: Linear Evaluation
            Dataset: ImageNet-1k
            Metrics:
              Top 1 Accuracy: 62.7
        Config: configs/benchmarks/classification/imagenet/resnet50_linear-8xb512-coslr-90e_in1k.py
        Weights: https://download.openmmlab.com/mmselfsup/1.x/simclr/simclr_resnet50_16xb256-coslr-200e_in1k/resnet50_linear-8xb512-coslr-90e_in1k/resnet50_linear-8xb512-coslr-90e_in1k_20220825-f12c0457.pth
  - Name: simclr_resnet50_16xb256-coslr-200e_in1k
    In Collection: SimCLR
    Metadata:
      Epochs: 200
      Batch Size: 4096
    Results: null
    Config: configs/selfsup/simclr/simclr_resnet50_16xb256-coslr-200e_in1k.py
    Weights: https://download.openmmlab.com/mmselfsup/1.x/simclr/simclr_resnet50_16xb256-coslr-200e_in1k/simclr_resnet50_16xb256-coslr-200e_in1k_20220825-4d9cce50.pth
    Downstream:
      - Type: Image Classification
        Metadata:
          Epochs: 90
          Batch Size: 4096
        Results:
          - Task: Linear Evaluation
            Dataset: ImageNet-1k
            Metrics:
              Top 1 Accuracy: 66.9
        Config: configs/benchmarks/classification/imagenet/resnet50_linear-8xb512-coslr-90e_in1k.py
        Weights: https://download.openmmlab.com/mmselfsup/1.x/simclr/simclr_resnet50_16xb256-coslr-200e_in1k/resnet50_linear-8xb512-coslr-90e_in1k/resnet50_linear-8xb512-coslr-90e_in1k_20220825-f12c0457.pth
  - Name: simclr_resnet50_16xb256-coslr-800e_in1k
    In Collection: SimCLR
    Metadata:
      Epochs: 200
      Batch Size: 4096
    Results: null
    Config: configs/selfsup/simclr/simclr_resnet50_16xb256-coslr-800e_in1k.py
    Weights: https://download.openmmlab.com/mmselfsup/1.x/simclr/simclr_resnet50_16xb256-coslr-800e_in1k/simclr_resnet50_16xb256-coslr-800e_in1k_20220825-85fcc4de.pth
    Downstream:
      - Type: Image Classification
        Metadata:
          Epochs: 90
          Batch Size: 4096
        Results:
          - Task: Linear Evaluation
            Dataset: ImageNet-1k
            Metrics:
              Top 1 Accuracy: 69.2
        Config: configs/benchmarks/classification/imagenet/resnet50_linear-8xb512-coslr-90e_in1k.py
        Weights: https://download.openmmlab.com/mmselfsup/1.x/simclr/simclr_resnet50_16xb256-coslr-800e_in1k/resnet50_linear-8xb512-coslr-90e_in1k/resnet50_linear-8xb512-coslr-90e_in1k_20220825-b80ae1e5.pth
