Collections:
  - Name: MaskFeat
    Metadata:
      Training Data: ImageNet-1k
      Training Techniques:
        - AdamW
      Training Resources: 8x A100-80G GPUs
      Architecture:
        - ViT
    Paper:
      Title: Masked Feature Prediction for Self-Supervised Visual Pre-Training
      URL: https://arxiv.org/abs/2112.09133v1
    README: configs/maskfeat/README.md

Models:
  - Name: maskfeat_vit-base-p16_8xb256-amp-coslr-300e_in1k
    Metadata:
      Epochs: 300
      Batch Size: 2048
      FLOPs: 17581972224
      Parameters: 85882692
      Training Data: ImageNet-1k
    In Collection: MaskFeat
    Results: null
    Weights: https://download.openmmlab.com/mmselfsup/1.x/maskfeat/maskfeat_vit-base-p16_8xb256-amp-coslr-300e_in1k/maskfeat_vit-base-p16_8xb256-amp-coslr-300e_in1k_20221101-6dfc8bf3.pth
    Config: configs/maskfeat/maskfeat_vit-base-p16_8xb256-amp-coslr-300e_in1k.py
    Downstream:
      - vit-base-p16_maskfeat-pre_8xb256-coslr-100e_in1k
  - Name: vit-base-p16_maskfeat-pre_8xb256-coslr-100e_in1k
    Metadata:
      Epochs: 100
      Batch Size: 2048
      FLOPs: 17581215744
      Parameters: 86566120
      Training Data: ImageNet-1k
    In Collection: MaskFeat
    Results:
      - Task: Image Classification
        Dataset: ImageNet-1k
        Metrics:
          Top 1 Accuracy: 83.4
    Weights: https://download.openmmlab.com/mmselfsup/1.x/maskfeat/maskfeat_vit-base-p16_8xb256-amp-coslr-300e_in1k/vit-base-p16_ft-8xb256-coslr-100e_in1k/vit-base-p16_ft-8xb256-coslr-100e_in1k_20221028-5134431c.pth
    Config: configs/maskfeat/benchmarks/vit-base-p16_8xb256-coslr-100e_in1k.py