hunk.code_window:         kwargs.pop('num_classes', 0)
           out_indices = kwargs.pop('out_indices', (0, 1, 2, 3, 4))
       model = ResNet(**kwargs)
       model.default_cfg = copy.deepcopy(default_cfg)
  
hunk.labels: keep add keep keep
hunk.after_edit:     pruned = kwargs.pop('pruned', False)
  
hunk.file_path: timm/models/resnet.py
hunk.type: add
file: # Validation Results
  
  This folder contains validation results for the models in this collection having pretrained weights. Since the focus for this repository is currently ImageNet-1k classification, all of the results are based on datasets compatible with ImageNet-1k classes.
  
  ## Datasets
  
  There are currently results for the ImageNet validation set and 3 additional test sets.
  
  ### ImageNet Validation - [`results-imagenet.csv`](results-imagenet.csv)
  
  * Source: http://image-net.org/challenges/LSVRC/2012/index
  * Paper: "ImageNet Large Scale Visual Recognition Challenge" - https://arxiv.org/abs/1409.0575
  
  The standard 50,000 image ImageNet-1k validation set. Model selection during training utilizes this validation set, so it is not a true test set. Question: Does anyone have the official ImageNet-1k test set classification labels now that challenges are done?
  
  ### ImageNetV2 Matched Frequency - [`results-imagenetv2-matched-frequency.csv`](results-imagenetv2-matched-frequency.csv)
  
  * Source: https://github.com/modestyachts/ImageNetV2
  * Paper: "Do ImageNet Classifiers Generalize to ImageNet?" - https://arxiv.org/abs/1902.10811
  
  An ImageNet test set of 10,000 images sampled from new images roughly 10 years after the original. Care was taken to replicate the original ImageNet curation/sampling process.
  
  ### ImageNet-Sketch - [`results-sketch.csv`](results-sketch.csv)
  
  * Source: https://github.com/HaohanWang/ImageNet-Sketch
  * Paper: "Learning Robust Global Representations by Penalizing Local Predictive Power" - https://arxiv.org/abs/1905.13549
  
  50,000 non photographic (or photos of such) images (sketches, doodles, mostly monochromatic) covering all 1000 ImageNet classes.
  
  ### ImageNet-Adversarial - [`results-imagenet-a.csv`](results-imagenet-a.csv)
  
  * Source: https://github.com/hendrycks/natural-adv-examples
  * Paper: "Natural Adversarial Examples" - https://arxiv.org/abs/1907.07174
  
  A collection of 7500 images covering 200 of the 1000 ImageNet classes. Images are naturally occuring adversarial examples that confuse typical ImageNet classifiers. This is a challenging dataset, your typical ResNet-50 will score 0% top-1.
  
  ## TODO
  * Add rank difference, and top-1/top-5 difference from ImageNet-1k validation for the 3 additional test sets
  * Explore adding a reduced version of ImageNet-C (Corruptions) and ImageNet-P (Perturbations) from https://github.com/hendrycks/robustness. The originals are huge and image size specific.
  