hunk.code_window:             attn_drop_rate (float): attention dropout rate
               drop_path_rate (float): stochastic depth rate
               weight_init (str): weight init scheme
               class_token (bool): use class token (default: False)
               rel_pos_ty pe (str): type of relative position
               shared_rel_pos (bool): share relative pos across all blocks
               fc_norm (bool): use pre classifier norm
               embed_layer (nn.Module): patch embedding layer
               norm_layer: (nn.Module): normalization layer
               act_layer: (nn.Module): MLP activation layer
           """
  
hunk.labels: keep keep keep replace replace replace replace keep keep keep keep
hunk.file_path: timm/models/vision_transformer_relpos.py
hunk.type: replace
file: # Res2Net
  
  **Res2Net** is an image model that employs a variation on bottleneck residual blocks, [Res2Net Blocks](https://paperswithcode.com/method/res2net-block). The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical residual-like connections within one single residual block. This represents multi-scale features at a granular level and increases the range of receptive fields for each network layer.
  
  ## How do I use this model on an image?
  To load a pretrained model:
  
  ```python
  import timm
  model = timm.create_model('res2net101_26w_4s', pretrained=True)
  model.eval()
  ```
  
  To load and preprocess the image:
  ```python 
  import urllib
  from PIL import Image
  from timm.data import resolve_data_config
  from timm.data.transforms_factory import create_transform
  
  config = resolve_data_config({}, model=model)
  transform = create_transform(**config)
  
  url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
  urllib.request.urlretrieve(url, filename)
  img = Image.open(filename).convert('RGB')
  tensor = transform(img).unsqueeze(0) # transform and add batch dimension
  ```
  
  To get the model predictions:
  ```python
  import torch
  with torch.no_grad():
      out = model(tensor)
  probabilities = torch.nn.functional.softmax(out[0], dim=0)
  print(probabilities.shape)
  # prints: torch.Size([1000])
  ```
  
  To get the top-5 predictions class names:
  ```python
  # Get imagenet class mappings
  url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
  urllib.request.urlretrieve(url, filename) 
  with open("imagenet_classes.txt", "r") as f:
      categories = [s.strip() for s in f.readlines()]
  
  # Print top categories per image
  top5_prob, top5_catid = torch.topk(probabilities, 5)
  for i in range(top5_prob.size(0)):
      print(categories[top5_catid[i]], top5_prob[i].item())
  # prints class names and probabilities like:
  # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
  ```
  
  Replace the model name with the variant you want to use, e.g. `res2net101_26w_4s`. You can find the IDs in the model summaries at the top of this page.
  
  To extract image features with this model, follow the [timm feature extraction examples](https://rwightman.github.io/pytorch-image-models/feature_extraction/), just change the name of the model you want to use.
  
  ## How do I finetune this model?
  You can finetune any of the pre-trained models just by changing the classifier (the last layer).
  ```python
  model = timm.create_model('res2net101_26w_4s', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
  ```
  To finetune on your own dataset, you have to write a training loop or adapt [timm's training
  script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
  
  ## 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
  @article{Gao_2021,
     title={Res2Net: A New Multi-Scale Backbone Architecture},
     volume={43},
     ISSN={1939-3539},
     url={http://dx.doi.org/10.1109/TPAMI.2019.2938758},
     DOI={10.1109/tpami.2019.2938758},
     number={2},
     journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
     publisher={Institute of Electrical and Electronics Engineers (IEEE)},
     author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip},
     year={2021},
     month={Feb},
     pages={652–662}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: Res2Net
    Paper:
      Title: 'Res2Net: A New Multi-scale Backbone Architecture'
      URL: https://paperswithcode.com/paper/res2net-a-new-multi-scale-backbone
  Models:
  - Name: res2net101_26w_4s
    In Collection: Res2Net
    Metadata:
      FLOPs: 10415881200
      Parameters: 45210000
      File Size: 181456059
      Architecture:
      - Batch Normalization
      - Convolution
      - Global Average Pooling
      - ReLU
      - Res2Net Block
      Tasks:
      - Image Classification
      Training Techniques:
      - SGD with Momentum
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x Titan Xp GPUs
      ID: res2net101_26w_4s
      LR: 0.1
      Epochs: 100
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.0001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L152
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net101_26w_4s-02a759a1.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.19%
        Top 5 Accuracy: 94.43%
  - Name: res2net50_14w_8s
    In Collection: Res2Net
    Metadata:
      FLOPs: 5403546768
      Parameters: 25060000
      File Size: 100638543
      Architecture:
      - Batch Normalization
      - Convolution
      - Global Average Pooling
      - ReLU
      - Res2Net Block
      Tasks:
      - Image Classification
      Training Techniques:
      - SGD with Momentum
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x Titan Xp GPUs
      ID: res2net50_14w_8s
      LR: 0.1
      Epochs: 100
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.0001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L196
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_14w_8s-6527dddc.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 78.14%
        Top 5 Accuracy: 93.86%
  - Name: res2net50_26w_4s
    In Collection: Res2Net
    Metadata:
      FLOPs: 5499974064
      Parameters: 25700000
      File Size: 103110087
      Architecture:
      - Batch Normalization
      - Convolution
      - Global Average Pooling
      - ReLU
      - Res2Net Block
      Tasks:
      - Image Classification
      Training Techniques:
      - SGD with Momentum
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x Titan Xp GPUs
      ID: res2net50_26w_4s
      LR: 0.1
      Epochs: 100
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.0001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L141
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_4s-06e79181.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 77.99%
        Top 5 Accuracy: 93.85%
  - Name: res2net50_26w_6s
    In Collection: Res2Net
    Metadata:
      FLOPs: 8130156528
      Parameters: 37050000
      File Size: 148603239
      Architecture:
      - Batch Normalization
      - Convolution
      - Global Average Pooling
      - ReLU
      - Res2Net Block
      Tasks:
      - Image Classification
      Training Techniques:
      - SGD with Momentum
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x Titan Xp GPUs
      ID: res2net50_26w_6s
      LR: 0.1
      Epochs: 100
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.0001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L163
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_6s-19041792.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 78.57%
        Top 5 Accuracy: 94.12%
  - Name: res2net50_26w_8s
    In Collection: Res2Net
    Metadata:
      FLOPs: 10760338992
      Parameters: 48400000
      File Size: 194085165
      Architecture:
      - Batch Normalization
      - Convolution
      - Global Average Pooling
      - ReLU
      - Res2Net Block
      Tasks:
      - Image Classification
      Training Techniques:
      - SGD with Momentum
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x Titan Xp GPUs
      ID: res2net50_26w_8s
      LR: 0.1
      Epochs: 100
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.0001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L174
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_8s-2c7c9f12.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.19%
        Top 5 Accuracy: 94.37%
  - Name: res2net50_48w_2s
    In Collection: Res2Net
    Metadata:
      FLOPs: 5375291520
      Parameters: 25290000
      File Size: 101421406
      Architecture:
      - Batch Normalization
      - Convolution
      - Global Average Pooling
      - ReLU
      - Res2Net Block
      Tasks:
      - Image Classification
      Training Techniques:
      - SGD with Momentum
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 4x Titan Xp GPUs
      ID: res2net50_48w_2s
      LR: 0.1
      Epochs: 100
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 256
      Image Size: '224'
      Weight Decay: 0.0001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L185
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_48w_2s-afed724a.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 77.53%
        Top 5 Accuracy: 93.56%
  -->