hunk.code_window: Hacked together by / Copyright 2020 Ross Wightman
   """
   import csv
   import os
   import wandb
   from collections import OrderedDict
  
hunk.labels: keep keep keep keep keep replace keep
hunk.file_path: timm/utils/summary.py
hunk.type: replace
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. 
  
  {% 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
  @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%
  -->
  