hunk.code_window:     if filter:
           models = fnmatch.filter(models, filter)  # include these models
       if exclude_filters:
           if not isinstance(exclude_filters, list):
               exclude_filters = [exclude_filters]
           for xf in exclude_filters:
               exclude_models = fnmatch.filter(models, xf)  # exclude these models
  
hunk.labels: keep keep keep replace keep keep keep
hunk.after_edit:         if not isinstance(exclude_filters, (tuple, list)):
  
hunk.file_path: timm/models/registry.py
hunk.type: replace
file: # TResNet
  
  A **TResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that aim to boost accuracy while maintaining GPU training and inference efficiency.  They contain several design tricks including a SpaceToDepth stem, [Anti-Alias downsampling](https://paperswithcode.com/method/anti-alias-downsampling), In-Place Activated BatchNorm, Blocks selection and [squeeze-and-excitation layers](https://paperswithcode.com/method/squeeze-and-excitation-block).
  
  ## How do I use this model on an image?
  To load a pretrained model:
  
  ```python
  import timm
  model = timm.create_model('tresnet_l', 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. `tresnet_l`. 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('tresnet_l', 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
  @misc{ridnik2020tresnet,
        title={TResNet: High Performance GPU-Dedicated Architecture}, 
        author={Tal Ridnik and Hussam Lawen and Asaf Noy and Emanuel Ben Baruch and Gilad Sharir and Itamar Friedman},
        year={2020},
        eprint={2003.13630},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: TResNet
    Paper:
      Title: 'TResNet: High Performance GPU-Dedicated Architecture'
      URL: https://paperswithcode.com/paper/tresnet-high-performance-gpu-dedicated
  Models:
  - Name: tresnet_l
    In Collection: TResNet
    Metadata:
      FLOPs: 10873416792
      Parameters: 53456696
      File Size: 224440219
      Architecture:
      - 1x1 Convolution
      - Anti-Alias Downsampling
      - Convolution
      - Global Average Pooling
      - InPlace-ABN
      - Leaky ReLU
      - ReLU
      - Residual Connection
      - Squeeze-and-Excitation Block
      Tasks:
      - Image Classification
      Training Techniques:
      - AutoAugment
      - Cutout
      - Label Smoothing
      - SGD with Momentum
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 8x NVIDIA 100 GPUs
      ID: tresnet_l
      LR: 0.01
      Epochs: 300
      Crop Pct: '0.875'
      Momentum: 0.9
      Image Size: '224'
      Weight Decay: 0.0001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L267
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_81_5-235b486c.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 81.49%
        Top 5 Accuracy: 95.62%
  - Name: tresnet_l_448
    In Collection: TResNet
    Metadata:
      FLOPs: 43488238584
      Parameters: 53456696
      File Size: 224440219
      Architecture:
      - 1x1 Convolution
      - Anti-Alias Downsampling
      - Convolution
      - Global Average Pooling
      - InPlace-ABN
      - Leaky ReLU
      - ReLU
      - Residual Connection
      - Squeeze-and-Excitation Block
      Tasks:
      - Image Classification
      Training Techniques:
      - AutoAugment
      - Cutout
      - Label Smoothing
      - SGD with Momentum
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 8x NVIDIA 100 GPUs
      ID: tresnet_l_448
      LR: 0.01
      Epochs: 300
      Crop Pct: '0.875'
      Momentum: 0.9
      Image Size: '448'
      Weight Decay: 0.0001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L285
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_448-940d0cd1.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 82.26%
        Top 5 Accuracy: 95.98%
  - Name: tresnet_m
    In Collection: TResNet
    Metadata:
      FLOPs: 5733048064
      Parameters: 41282200
      File Size: 125861314
      Architecture:
      - 1x1 Convolution
      - Anti-Alias Downsampling
      - Convolution
      - Global Average Pooling
      - InPlace-ABN
      - Leaky ReLU
      - ReLU
      - Residual Connection
      - Squeeze-and-Excitation Block
      Tasks:
      - Image Classification
      Training Techniques:
      - AutoAugment
      - Cutout
      - Label Smoothing
      - SGD with Momentum
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 8x NVIDIA 100 GPUs
      Training Time: < 24 hours
      ID: tresnet_m
      LR: 0.01
      Epochs: 300
      Crop Pct: '0.875'
      Momentum: 0.9
      Image Size: '224'
      Weight Decay: 0.0001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L261
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_m_80_8-dbc13962.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 80.8%
        Top 5 Accuracy: 94.86%
  - Name: tresnet_m_448
    In Collection: TResNet
    Metadata:
      FLOPs: 22929743104
      Parameters: 29278464
      File Size: 125861314
      Architecture:
      - 1x1 Convolution
      - Anti-Alias Downsampling
      - Convolution
      - Global Average Pooling
      - InPlace-ABN
      - Leaky ReLU
      - ReLU
      - Residual Connection
      - Squeeze-and-Excitation Block
      Tasks:
      - Image Classification
      Training Techniques:
      - AutoAugment
      - Cutout
      - Label Smoothing
      - SGD with Momentum
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 8x NVIDIA 100 GPUs
      ID: tresnet_m_448
      LR: 0.01
      Epochs: 300
      Crop Pct: '0.875'
      Momentum: 0.9
      Image Size: '448'
      Weight Decay: 0.0001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L279
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_m_448-bc359d10.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 81.72%
        Top 5 Accuracy: 95.57%
  - Name: tresnet_xl
    In Collection: TResNet
    Metadata:
      FLOPs: 15162534034
      Parameters: 75646610
      File Size: 314378965
      Architecture:
      - 1x1 Convolution
      - Anti-Alias Downsampling
      - Convolution
      - Global Average Pooling
      - InPlace-ABN
      - Leaky ReLU
      - ReLU
      - Residual Connection
      - Squeeze-and-Excitation Block
      Tasks:
      - Image Classification
      Training Techniques:
      - AutoAugment
      - Cutout
      - Label Smoothing
      - SGD with Momentum
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 8x NVIDIA 100 GPUs
      ID: tresnet_xl
      LR: 0.01
      Epochs: 300
      Crop Pct: '0.875'
      Momentum: 0.9
      Image Size: '224'
      Weight Decay: 0.0001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L273
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_xl_82_0-a2d51b00.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 82.05%
        Top 5 Accuracy: 95.93%
  - Name: tresnet_xl_448
    In Collection: TResNet
    Metadata:
      FLOPs: 60641712730
      Parameters: 75646610
      File Size: 224440219
      Architecture:
      - 1x1 Convolution
      - Anti-Alias Downsampling
      - Convolution
      - Global Average Pooling
      - InPlace-ABN
      - Leaky ReLU
      - ReLU
      - Residual Connection
      - Squeeze-and-Excitation Block
      Tasks:
      - Image Classification
      Training Techniques:
      - AutoAugment
      - Cutout
      - Label Smoothing
      - SGD with Momentum
      - Weight Decay
      Training Data:
      - ImageNet
      Training Resources: 8x NVIDIA 100 GPUs
      ID: tresnet_xl_448
      LR: 0.01
      Epochs: 300
      Crop Pct: '0.875'
      Momentum: 0.9
      Image Size: '448'
      Weight Decay: 0.0001
      Interpolation: bilinear
    Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L291
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_448-940d0cd1.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 83.06%
        Top 5 Accuracy: 96.19%
  -->