hunk.code_window:     return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
   def list_models(filter='', module='', pretrained=False, exclude_filters=''):
       """ Return list of available model names, sorted alphabetically
       Args:
  
hunk.labels: keep keep keep replace keep keep keep
hunk.after_edit: def list_models(filter='', module='', pretrained=False, exclude_filters='', name_matches_cfg=False):
  
hunk.file_path: timm/models/registry.py
hunk.type: replace
file: # AdvProp (EfficientNet)
  
  **AdvProp** is an adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. Key to the method is the usage of a separate auxiliary batch norm for adversarial examples, as they have different underlying distributions to normal examples.
  
  The weights from this model were ported from [Tensorflow/TPU](https://github.com/tensorflow/tpu).
  
  {% include 'code_snippets.md' %}
  
  ## How do I train this model?
  
  You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
  
  ## Citation
  
  ```BibTeX
  @misc{xie2020adversarial,
        title={Adversarial Examples Improve Image Recognition}, 
        author={Cihang Xie and Mingxing Tan and Boqing Gong and Jiang Wang and Alan Yuille and Quoc V. Le},
        year={2020},
        eprint={1911.09665},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: AdvProp
    Paper:
      Title: Adversarial Examples Improve Image Recognition
      URL: https://paperswithcode.com/paper/adversarial-examples-improve-image
  Models:
  - Name: tf_efficientnet_b0_ap
    In Collection: AdvProp
    Metadata:
      FLOPs: 488688572
      Parameters: 5290000
      File Size: 21385973
      Architecture:
      - 1x1 Convolution
      - Average Pooling
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Inverted Residual Block
      - Squeeze-and-Excitation Block
      - Swish
      Tasks:
      - Image Classification
      Training Techniques:
      - AdvProp
      - AutoAugment
      - Label Smoothing
      - RMSProp
      - Stochastic Depth
      - Weight Decay
      Training Data:
      - ImageNet
      ID: tf_efficientnet_b0_ap
      LR: 0.256
      Epochs: 350
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 2048
      Image Size: '224'
      Weight Decay: 1.0e-05
      Interpolation: bicubic
      RMSProp Decay: 0.9
      Label Smoothing: 0.1
      BatchNorm Momentum: 0.99
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1334
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ap-f262efe1.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 77.1%
        Top 5 Accuracy: 93.26%
  - Name: tf_efficientnet_b1_ap
    In Collection: AdvProp
    Metadata:
      FLOPs: 883633200
      Parameters: 7790000
      File Size: 31515350
      Architecture:
      - 1x1 Convolution
      - Average Pooling
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Inverted Residual Block
      - Squeeze-and-Excitation Block
      - Swish
      Tasks:
      - Image Classification
      Training Techniques:
      - AdvProp
      - AutoAugment
      - Label Smoothing
      - RMSProp
      - Stochastic Depth
      - Weight Decay
      Training Data:
      - ImageNet
      ID: tf_efficientnet_b1_ap
      LR: 0.256
      Epochs: 350
      Crop Pct: '0.882'
      Momentum: 0.9
      Batch Size: 2048
      Image Size: '240'
      Weight Decay: 1.0e-05
      Interpolation: bicubic
      RMSProp Decay: 0.9
      Label Smoothing: 0.1
      BatchNorm Momentum: 0.99
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1344
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_ap-44ef0a3d.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.28%
        Top 5 Accuracy: 94.3%
  - Name: tf_efficientnet_b2_ap
    In Collection: AdvProp
    Metadata:
      FLOPs: 1234321170
      Parameters: 9110000
      File Size: 36800745
      Architecture:
      - 1x1 Convolution
      - Average Pooling
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Inverted Residual Block
      - Squeeze-and-Excitation Block
      - Swish
      Tasks:
      - Image Classification
      Training Techniques:
      - AdvProp
      - AutoAugment
      - Label Smoothing
      - RMSProp
      - Stochastic Depth
      - Weight Decay
      Training Data:
      - ImageNet
      ID: tf_efficientnet_b2_ap
      LR: 0.256
      Epochs: 350
      Crop Pct: '0.89'
      Momentum: 0.9
      Batch Size: 2048
      Image Size: '260'
      Weight Decay: 1.0e-05
      Interpolation: bicubic
      RMSProp Decay: 0.9
      Label Smoothing: 0.1
      BatchNorm Momentum: 0.99
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1354
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_ap-2f8e7636.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 80.3%
        Top 5 Accuracy: 95.03%
  - Name: tf_efficientnet_b3_ap
    In Collection: AdvProp
    Metadata:
      FLOPs: 2275247568
      Parameters: 12230000
      File Size: 49384538
      Architecture:
      - 1x1 Convolution
      - Average Pooling
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Inverted Residual Block
      - Squeeze-and-Excitation Block
      - Swish
      Tasks:
      - Image Classification
      Training Techniques:
      - AdvProp
      - AutoAugment
      - Label Smoothing
      - RMSProp
      - Stochastic Depth
      - Weight Decay
      Training Data:
      - ImageNet
      ID: tf_efficientnet_b3_ap
      LR: 0.256
      Epochs: 350
      Crop Pct: '0.904'
      Momentum: 0.9
      Batch Size: 2048
      Image Size: '300'
      Weight Decay: 1.0e-05
      Interpolation: bicubic
      RMSProp Decay: 0.9
      Label Smoothing: 0.1
      BatchNorm Momentum: 0.99
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1364
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ap-aad25bdd.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 81.82%
        Top 5 Accuracy: 95.62%
  - Name: tf_efficientnet_b4_ap
    In Collection: AdvProp
    Metadata:
      FLOPs: 5749638672
      Parameters: 19340000
      File Size: 77993585
      Architecture:
      - 1x1 Convolution
      - Average Pooling
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Inverted Residual Block
      - Squeeze-and-Excitation Block
      - Swish
      Tasks:
      - Image Classification
      Training Techniques:
      - AdvProp
      - AutoAugment
      - Label Smoothing
      - RMSProp
      - Stochastic Depth
      - Weight Decay
      Training Data:
      - ImageNet
      ID: tf_efficientnet_b4_ap
      LR: 0.256
      Epochs: 350
      Crop Pct: '0.922'
      Momentum: 0.9
      Batch Size: 2048
      Image Size: '380'
      Weight Decay: 1.0e-05
      Interpolation: bicubic
      RMSProp Decay: 0.9
      Label Smoothing: 0.1
      BatchNorm Momentum: 0.99
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1374
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_ap-dedb23e6.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 83.26%
        Top 5 Accuracy: 96.39%
  - Name: tf_efficientnet_b5_ap
    In Collection: AdvProp
    Metadata:
      FLOPs: 13176501888
      Parameters: 30390000
      File Size: 122403150
      Architecture:
      - 1x1 Convolution
      - Average Pooling
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Inverted Residual Block
      - Squeeze-and-Excitation Block
      - Swish
      Tasks:
      - Image Classification
      Training Techniques:
      - AdvProp
      - AutoAugment
      - Label Smoothing
      - RMSProp
      - Stochastic Depth
      - Weight Decay
      Training Data:
      - ImageNet
      ID: tf_efficientnet_b5_ap
      LR: 0.256
      Epochs: 350
      Crop Pct: '0.934'
      Momentum: 0.9
      Batch Size: 2048
      Image Size: '456'
      Weight Decay: 1.0e-05
      Interpolation: bicubic
      RMSProp Decay: 0.9
      Label Smoothing: 0.1
      BatchNorm Momentum: 0.99
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1384
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ap-9e82fae8.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 84.25%
        Top 5 Accuracy: 96.97%
  - Name: tf_efficientnet_b6_ap
    In Collection: AdvProp
    Metadata:
      FLOPs: 24180518488
      Parameters: 43040000
      File Size: 173237466
      Architecture:
      - 1x1 Convolution
      - Average Pooling
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Inverted Residual Block
      - Squeeze-and-Excitation Block
      - Swish
      Tasks:
      - Image Classification
      Training Techniques:
      - AdvProp
      - AutoAugment
      - Label Smoothing
      - RMSProp
      - Stochastic Depth
      - Weight Decay
      Training Data:
      - ImageNet
      ID: tf_efficientnet_b6_ap
      LR: 0.256
      Epochs: 350
      Crop Pct: '0.942'
      Momentum: 0.9
      Batch Size: 2048
      Image Size: '528'
      Weight Decay: 1.0e-05
      Interpolation: bicubic
      RMSProp Decay: 0.9
      Label Smoothing: 0.1
      BatchNorm Momentum: 0.99
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1394
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_ap-4ffb161f.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 84.79%
        Top 5 Accuracy: 97.14%
  - Name: tf_efficientnet_b7_ap
    In Collection: AdvProp
    Metadata:
      FLOPs: 48205304880
      Parameters: 66349999
      File Size: 266850607
      Architecture:
      - 1x1 Convolution
      - Average Pooling
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Inverted Residual Block
      - Squeeze-and-Excitation Block
      - Swish
      Tasks:
      - Image Classification
      Training Techniques:
      - AdvProp
      - AutoAugment
      - Label Smoothing
      - RMSProp
      - Stochastic Depth
      - Weight Decay
      Training Data:
      - ImageNet
      ID: tf_efficientnet_b7_ap
      LR: 0.256
      Epochs: 350
      Crop Pct: '0.949'
      Momentum: 0.9
      Batch Size: 2048
      Image Size: '600'
      Weight Decay: 1.0e-05
      Interpolation: bicubic
      RMSProp Decay: 0.9
      Label Smoothing: 0.1
      BatchNorm Momentum: 0.99
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1405
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ap-ddb28fec.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 85.12%
        Top 5 Accuracy: 97.25%
  - Name: tf_efficientnet_b8_ap
    In Collection: AdvProp
    Metadata:
      FLOPs: 80962956270
      Parameters: 87410000
      File Size: 351412563
      Architecture:
      - 1x1 Convolution
      - Average Pooling
      - Batch Normalization
      - Convolution
      - Dense Connections
      - Dropout
      - Inverted Residual Block
      - Squeeze-and-Excitation Block
      - Swish
      Tasks:
      - Image Classification
      Training Techniques:
      - AdvProp
      - AutoAugment
      - Label Smoothing
      - RMSProp
      - Stochastic Depth
      - Weight Decay
      Training Data:
      - ImageNet
      ID: tf_efficientnet_b8_ap
      LR: 0.128
      Epochs: 350
      Crop Pct: '0.954'
      Momentum: 0.9
      Batch Size: 2048
      Image Size: '672'
      Weight Decay: 1.0e-05
      Interpolation: bicubic
      RMSProp Decay: 0.9
      Label Smoothing: 0.1
      BatchNorm Momentum: 0.99
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1416
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ap-00e169fa.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 85.37%
        Top 5 Accuracy: 97.3%
  -->
  