Collections:
  - Name: MixMIM
    Metadata:
      Architecture:
        - Attention Dropout
        - Convolution
        - Dense Connections
        - Dropout
        - GELU
        - Layer Normalization
        - Multi-Head Attention
        - Scaled Dot-Product Attention
        - Tanh Activation
    Paper:
      Title: 'MixMIM: Mixed and Masked Image Modeling for Efficient Visual Representation Learning'
      URL: https://arxiv.org/abs/2205.13137
    README: configs/mixmim/README.md
    Code:
      URL: https://github.com/open-mmlab/mmclassification/blob/dev-1.x/mmcls/models/backbones/mixmim.py
      Version: v1.0.0rc4

Models:
  - Name: mixmim-base_3rdparty_in1k
    Metadata:
      FLOPs: 16352000000
      Parameters: 88344000
      Training Data:
        - ImageNet-1k
    In Collection: MixMIM
    Results:
      - Dataset: ImageNet-1k
        Task: Image Classification
        Metrics:
          Top 1 Accuracy: 84.6
          Top 5 Accuracy: 97.0
    Weights: https://download.openmmlab.com/mmclassification/v0/mixmim/mixmim-base_3rdparty_in1k_20221206-e40e2c8c.pth
    Config: configs/mixmim/mixmim-base_8xb64_in1k.py
    Converted From:
      Code: https://github.com/Sense-X/MixMIM
