hunk.code_window: def update_summary(epoch, train_metrics, eval_metrics, filename, write_header=False, log_wandb=False):
       rowd = OrderedDict(epoch=epoch)
       rowd.update([('train_' + k, v) for k, v in train_metrics.items()])
       rowd.update([('eval_' + k, v) for k, v in eval_metrics.items()])
       if log_wandb:
           wandb.log(rowd)
       with open(filename, mode='a') as cf:
           dw = csv.DictWriter(cf, fieldnames=rowd.keys())
           if write_header:  # first iteration (epoch == 1 can't be used)
  
hunk.labels: keep keep keep keep keep replace replace keep keep keep
hunk.file_path: timm/utils/summary.py
hunk.type: replace
file: # (Gluon) SE-ResNeXt
  
  **SE ResNeXt** is a variant of a [ResNext](https://www.paperswithcode.com/method/resnext) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
  
  The weights from this model were ported from [Gluon](https://cv.gluon.ai/model_zoo/classification.html).
  
  {% 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{hu2019squeezeandexcitation,
        title={Squeeze-and-Excitation Networks}, 
        author={Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu},
        year={2019},
        eprint={1709.01507},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: Gloun SEResNeXt
    Paper:
      Title: Squeeze-and-Excitation Networks
      URL: https://paperswithcode.com/paper/squeeze-and-excitation-networks
  Models:
  - Name: gluon_seresnext101_32x4d
    In Collection: Gloun SEResNeXt
    Metadata:
      FLOPs: 10302923504
      Parameters: 48960000
      File Size: 196505510
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Convolution
      - Global Average Pooling
      - Grouped Convolution
      - Max Pooling
      - ReLU
      - ResNeXt Block
      - Residual Connection
      - Softmax
      - Squeeze-and-Excitation Block
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: gluon_seresnext101_32x4d
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L219
    Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_32x4d-cf52900d.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 80.87%
        Top 5 Accuracy: 95.29%
  - Name: gluon_seresnext101_64x4d
    In Collection: Gloun SEResNeXt
    Metadata:
      FLOPs: 19958950640
      Parameters: 88230000
      File Size: 353875948
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Convolution
      - Global Average Pooling
      - Grouped Convolution
      - Max Pooling
      - ReLU
      - ResNeXt Block
      - Residual Connection
      - Softmax
      - Squeeze-and-Excitation Block
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: gluon_seresnext101_64x4d
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L229
    Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_64x4d-f9926f93.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 80.88%
        Top 5 Accuracy: 95.31%
  - Name: gluon_seresnext50_32x4d
    In Collection: Gloun SEResNeXt
    Metadata:
      FLOPs: 5475179184
      Parameters: 27560000
      File Size: 110578827
      Architecture:
      - 1x1 Convolution
      - Batch Normalization
      - Convolution
      - Global Average Pooling
      - Grouped Convolution
      - Max Pooling
      - ReLU
      - ResNeXt Block
      - Residual Connection
      - Softmax
      - Squeeze-and-Excitation Block
      Tasks:
      - Image Classification
      Training Data:
      - ImageNet
      ID: gluon_seresnext50_32x4d
      Crop Pct: '0.875'
      Image Size: '224'
      Interpolation: bicubic
    Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L209
    Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext50_32x4d-90cf2d6e.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.92%
        Top 5 Accuracy: 94.82%
  -->
  