X_SSL_Net/tmp/VMamba-main/analyze/mmpretrain_configs/configs/mff/metafile.yml

Collections:
  - Name: MFF
    Metadata:
      Training Data: ImageNet-1k
      Training Techniques:
        - AdamW
      Training Resources: 8x A100-80G GPUs
      Architecture:
        - ViT
    Paper:
      Title: Improving Pixel-based MIM by Reducing Wasted Modeling Capability
      URL: https://arxiv.org/pdf/2308.00261.pdf
    README: configs/mff/README.md

Models:
  - Name: mff_vit-base-p16_8xb512-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/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k_20230801-3c1bcce4.pth
    Config: configs/mff/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k.py
    Downstream:
      - vit-base-p16_mff-300e-pre_8xb128-coslr-100e_in1k
      - vit-base-p16_mff-300e-pre_8xb2048-linear-coslr-90e_in1k
  - Name: mff_vit-base-p16_8xb512-amp-coslr-800e_in1k
    Metadata:
      Epochs: 800
      Batch Size: 2048
      FLOPs: 17581972224
      Parameters: 85882692
      Training Data: ImageNet-1k
    In Collection: MaskFeat
    Results: null
    Weights: https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-800e_in1k/mff_vit-base-p16_8xb512-amp-coslr-800e_in1k_20230801-3af7cd9d.pth
    Config: configs/mff/mff_vit-base-p16_8xb512-amp-coslr-800e_in1k.py
    Downstream:
      - vit-base-p16_mff-800e-pre_8xb128-coslr-100e_in1k
      - vit-base-p16_mff-800e-pre_8xb2048-linear-coslr-90e_in1k
  - Name: vit-base-p16_mff-300e-pre_8xb128-coslr-100e_in1k
    Metadata:
      Epochs: 100
      Batch Size: 1024
      FLOPs: 17581215744
      Parameters: 86566120
      Training Data: ImageNet-1k
    In Collection: MaskFeat
    Results:
      - Task: Image Classification
        Dataset: ImageNet-1k
        Metrics:
          Top 1 Accuracy: 83.0
    Weights: https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k/vit-base-p16_8xb128-coslr-100e_in1k/vit-base-p16_8xb128-coslr-100e_in1k_20230802-d746fdb7.pth
    Config: configs/mff/benchmarks/vit-base-p16_8xb128-coslr-100e_in1k.py
  - Name: vit-base-p16_mff-800e-pre_8xb128-coslr-100e_in1k
    Metadata:
      Epochs: 100
      Batch Size: 1024
      FLOPs: 17581215744
      Parameters: 86566120
      Training Data: ImageNet-1k
    In Collection: MFF
    Results:
      - Task: Image Classification
        Dataset: ImageNet-1k
        Metrics:
          Top 1 Accuracy: 83.7
    Weights: https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-800e_in1k/vit-base-p16_8xb128-coslr-100e/vit-base-p16_8xb128-coslr-100e_20230802-6780e47d.pth
    Config: configs/mff/benchmarks/vit-base-p16_8xb128-coslr-100e_in1k.py
  - Name: vit-base-p16_mff-300e-pre_8xb2048-linear-coslr-90e_in1k
    Metadata:
      Epochs: 90
      Batch Size: 16384
      FLOPs: 17581215744
      Parameters: 86566120
      Training Data: ImageNet-1k
    In Collection: MFF
    Results:
      - Task: Image Classification
        Dataset: ImageNet-1k
        Metrics:
          Top 1 Accuracy: 64.2
    Weights:
    Config: configs/mff/benchmarks/vit-base-p16_8xb2048-linear-coslr-90e_in1k.py
  - Name: vit-base-p16_mff-800e-pre_8xb2048-linear-coslr-90e_in1k
    Metadata:
      Epochs: 90
      Batch Size: 16384
      FLOPs: 17581215744
      Parameters: 86566120
      Training Data: ImageNet-1k
    In Collection: MFF
    Results:
      - Task: Image Classification
        Dataset: ImageNet-1k
        Metrics:
          Top 1 Accuracy: 68.3
    Weights: https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k/vit-base-p16_8xb128-coslr-100e_in1k/vit-base-p16_8xb128-coslr-100e_in1k_20230802-d746fdb7.pth
    Config: configs/mff/benchmarks/vit-base-p16_8xb2048-linear-coslr-90e_in1k.py