hunk.code_window:                 models = set(models).difference(exclude_models)
       if pretrained:
           models = _model_has_pretrained.intersection(models)
       return list(sorted(models, key=_natural_key))
   def is_model(model_name):
       """ Check if a model name exists
  
hunk.labels: keep keep add keep keep keep keep keep
hunk.after_edit:     if name_matches_cfg:
           models = set(_model_default_cfgs).intersection(models)
  
hunk.file_path: timm/models/registry.py
hunk.type: add
file: # ResNeXt
  
  A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension,  *cardinality* (the size of the set of transformations) $C$, as an essential factor in addition to the dimensions of depth and width. 
  
  ## How do I use this model on an image?
  To load a pretrained model:
  
  ```python
  import timm
  model = timm.create_model('resnext101_32x8d', 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. `resnext101_32x8d`. 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('resnext101_32x8d', 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{DBLP:journals/corr/XieGDTH16,
    author    = {Saining Xie and
                 Ross B. Girshick and
                 Piotr Doll{\'{a}}r and
                 Zhuowen Tu and
                 Kaiming He},
    title     = {Aggregated Residual Transformations for Deep Neural Networks},
    journal   = {CoRR},
    volume    = {abs/1611.05431},
    year      = {2016},
    url       = {http://arxiv.org/abs/1611.05431},
    archivePrefix = {arXiv},
    eprint    = {1611.05431},
    timestamp = {Mon, 13 Aug 2018 16:45:58 +0200},
    biburl    = {https://dblp.org/rec/journals/corr/XieGDTH16.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: ResNeXt
    Paper:
      Title: Aggregated Residual Transformations for Deep Neural Networks
      URL: https://paperswithcode.com/paper/aggregated-residual-transformations-for-deep
  Models:
  - Name: resnext101_32x8d
    In Collection: ResNeXt
    Metadata:
      FLOPs: 21180417024
      Parameters: 88790000
      File Size: 356082095
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Convolution
      - Global Average Pooling
      - Grouped Convolution
      - Max Pooling
      - ReLU
      - ResNeXt Block
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: resnext101_32x8d
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnet.py#L877
    Weights: https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.3%
        Top 5 Accuracy: 94.53%
  - Name: resnext50_32x4d
    In Collection: ResNeXt
    Metadata:
      FLOPs: 5472648192
      Parameters: 25030000
      File Size: 100435887
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Convolution
      - Global Average Pooling
      - Grouped Convolution
      - Max Pooling
      - ReLU
      - ResNeXt Block
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: resnext50_32x4d
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnet.py#L851
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnext50_32x4d_ra-d733960d.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.79%
        Top 5 Accuracy: 94.61%
  - Name: resnext50d_32x4d
    In Collection: ResNeXt
    Metadata:
      FLOPs: 5781119488
      Parameters: 25050000
      File Size: 100515304
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Convolution
      - Global Average Pooling
      - Grouped Convolution
      - Max Pooling
      - ReLU
      - ResNeXt Block
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: resnext50d_32x4d
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnet.py#L869
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnext50d_32x4d-103e99f8.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.67%
        Top 5 Accuracy: 94.87%
  - Name: tv_resnext50_32x4d
    In Collection: ResNeXt
    Metadata:
      FLOPs: 5472648192
      Parameters: 25030000
      File Size: 100441675
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Convolution
      - Global Average Pooling
      - Grouped Convolution
      - Max Pooling
      - ReLU
      - ResNeXt Block
      - Residual Connection
      - Softmax
      Tasks:
      - Image Classification
      Training Techniques:
      - SGD with Momentum
      - Weight Decay
      Training Data:
      - ImageNet
      ID: tv_resnext50_32x4d
      LR: 0.1
      Epochs: 90
      Crop Pct: '0.875'
      LR Gamma: 0.1
      Momentum: 0.9
      Batch Size: 32
      Image Size: '224'
      LR Step Size: 30
      Weight Decay: 0.0001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L842
    Weights: https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 77.61%
        Top 5 Accuracy: 93.68%
  -->