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: # HRNet
  
  **HRNet**, or **High-Resolution Net**, is a general purpose convolutional neural network for tasks like semantic segmentation, object detection and image classification. It is able to maintain high resolution representations through the whole process. We start from a high-resolution convolution stream, gradually add high-to-low resolution convolution streams one by one, and connect the multi-resolution streams in parallel. The resulting network consists of several ($4$ in the paper) stages and the $n$th stage contains $n$ streams corresponding to $n$ resolutions. The authors conduct repeated multi-resolution fusions by exchanging the information across the parallel streams over and over.
  
  {% 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{sun2019highresolution,
        title={High-Resolution Representations for Labeling Pixels and Regions}, 
        author={Ke Sun and Yang Zhao and Borui Jiang and Tianheng Cheng and Bin Xiao and Dong Liu and Yadong Mu and Xinggang Wang and Wenyu Liu and Jingdong Wang},
        year={2019},
        eprint={1904.04514},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: HRNet
    Paper:
      Title: Deep High-Resolution Representation Learning for Visual Recognition
      URL: https://paperswithcode.com/paper/190807919
  Models:
  - Name: hrnet_w18
    In Collection: HRNet
    Metadata:
      FLOPs: 5547205500
      Parameters: 21300000
      File Size: 85718883
      Architecture:
      - Batch Normalization
      - Convolution
      - ReLU
      - Residual Connection
      Tasks:
      - Image Classification
      Training Techniques:
      - Nesterov Accelerated Gradient
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x NVIDIA V100 GPUs
      ID: hrnet_w18
      Epochs: 100
      Layers: 18
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L800
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w18-8cb57bb9.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 76.76%
        Top 5 Accuracy: 93.44%
  - Name: hrnet_w18_small
    In Collection: HRNet
    Metadata:
      FLOPs: 2071651488
      Parameters: 13190000
      File Size: 52934302
      Architecture:
      - Batch Normalization
      - Convolution
      - ReLU
      - Residual Connection
      Tasks:
      - Image Classification
      Training Techniques:
      - Nesterov Accelerated Gradient
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x NVIDIA V100 GPUs
      ID: hrnet_w18_small
      Epochs: 100
      Layers: 18
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L790
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnet_w18_small_v1-f460c6bc.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 72.34%
        Top 5 Accuracy: 90.68%
  - Name: hrnet_w18_small_v2
    In Collection: HRNet
    Metadata:
      FLOPs: 3360023160
      Parameters: 15600000
      File Size: 62682879
      Architecture:
      - Batch Normalization
      - Convolution
      - ReLU
      - Residual Connection
      Tasks:
      - Image Classification
      Training Techniques:
      - Nesterov Accelerated Gradient
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x NVIDIA V100 GPUs
      ID: hrnet_w18_small_v2
      Epochs: 100
      Layers: 18
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L795
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnet_w18_small_v2-4c50a8cb.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 75.11%
        Top 5 Accuracy: 92.41%
  - Name: hrnet_w30
    In Collection: HRNet
    Metadata:
      FLOPs: 10474119492
      Parameters: 37710000
      File Size: 151452218
      Architecture:
      - Batch Normalization
      - Convolution
      - ReLU
      - Residual Connection
      Tasks:
      - Image Classification
      Training Techniques:
      - Nesterov Accelerated Gradient
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x NVIDIA V100 GPUs
      ID: hrnet_w30
      Epochs: 100
      Layers: 30
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L805
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w30-8d7f8dab.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 78.21%
        Top 5 Accuracy: 94.22%
  - Name: hrnet_w32
    In Collection: HRNet
    Metadata:
      FLOPs: 11524528320
      Parameters: 41230000
      File Size: 165547812
      Architecture:
      - Batch Normalization
      - Convolution
      - ReLU
      - Residual Connection
      Tasks:
      - Image Classification
      Training Techniques:
      - Nesterov Accelerated Gradient
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x NVIDIA V100 GPUs
      Training Time: 60 hours
      ID: hrnet_w32
      Epochs: 100
      Layers: 32
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L810
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w32-90d8c5fb.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 78.45%
        Top 5 Accuracy: 94.19%
  - Name: hrnet_w40
    In Collection: HRNet
    Metadata:
      FLOPs: 16381182192
      Parameters: 57560000
      File Size: 230899236
      Architecture:
      - Batch Normalization
      - Convolution
      - ReLU
      - Residual Connection
      Tasks:
      - Image Classification
      Training Techniques:
      - Nesterov Accelerated Gradient
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x NVIDIA V100 GPUs
      ID: hrnet_w40
      Epochs: 100
      Layers: 40
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L815
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w40-7cd397a4.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 78.93%
        Top 5 Accuracy: 94.48%
  - Name: hrnet_w44
    In Collection: HRNet
    Metadata:
      FLOPs: 19202520264
      Parameters: 67060000
      File Size: 268957432
      Architecture:
      - Batch Normalization
      - Convolution
      - ReLU
      - Residual Connection
      Tasks:
      - Image Classification
      Training Techniques:
      - Nesterov Accelerated Gradient
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x NVIDIA V100 GPUs
      ID: hrnet_w44
      Epochs: 100
      Layers: 44
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L820
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w44-c9ac8c18.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 78.89%
        Top 5 Accuracy: 94.37%
  - Name: hrnet_w48
    In Collection: HRNet
    Metadata:
      FLOPs: 22285865760
      Parameters: 77470000
      File Size: 310603710
      Architecture:
      - Batch Normalization
      - Convolution
      - ReLU
      - Residual Connection
      Tasks:
      - Image Classification
      Training Techniques:
      - Nesterov Accelerated Gradient
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x NVIDIA V100 GPUs
      Training Time: 80 hours
      ID: hrnet_w48
      Epochs: 100
      Layers: 48
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L825
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w48-abd2e6ab.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.32%
        Top 5 Accuracy: 94.51%
  - Name: hrnet_w64
    In Collection: HRNet
    Metadata:
      FLOPs: 37239321984
      Parameters: 128060000
      File Size: 513071818
      Architecture:
      - Batch Normalization
      - Convolution
      - ReLU
      - Residual Connection
      Tasks:
      - Image Classification
      Training Techniques:
      - Nesterov Accelerated Gradient
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x NVIDIA V100 GPUs
      ID: hrnet_w64
      Epochs: 100
      Layers: 64
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L830
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w64-b47cc881.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.46%
        Top 5 Accuracy: 94.65%
  -->
  