mmpretrain/configs/cae/metafile.yml

Collections:
  - Name: CAE
    Metadata:
      Training Data: ImageNet-1k
      Training Techniques:
        - AdamW
      Training Resources: 8x A100-80G GPUs
      Architecture:
        - ViT
    Paper:
        URL: https://arxiv.org/abs/2202.03026
        Title: "Context Autoencoder for Self-Supervised Representation Learning"
    README: configs/cae/README.md

Models:
  - Name: cae_vit-base-p16_8xb256-amp-coslr-300e_in1k
    In Collection: CAE
    Metadata:
      Epochs: 300
      Batch Size: 2048
    Results: null
    Config: configs/cae/cae_vit-base-p16_8xb256-amp-coslr-300e_in1k.py
    Weights: https://download.openmmlab.com/mmselfsup/1.x/cae/cae_vit-base-p16_8xb256-amp-coslr-300e_in1k/cae_vit-base-p16_8xb256-amp-coslr-300e_in1k_20221230-808170f3.pth
    Downstream:
      - beit-base-p16_cae-pre_8xb128-coslr-100e_in1k
  - Name: beit-base-p16_cae-pre_8xb128-coslr-100e_in1k
    In Collection: CAE
    Metadata:
      Epochs: 100
      Batch Size: 1024
    Results:
      - Task: Image Classification
        Dataset: ImageNet-1k
        Metrics:
          Top 1 Accuracy: 83.2
    Config: configs/cae/benchmarks/beit-base-p16_8xb128-coslr-100e_in1k.py
    Weights: https://download.openmmlab.com/mmselfsup/1.x/cae/cae_vit-base-p16_16xb128-fp16-coslr-300e_in1k/vit-base-p16_ft-8xb128-coslr-100e-rpe_in1k/vit-base-p16_ft-8xb128-coslr-100e-rpe_in1k_20220825-f3d234cd.pth