Wide ResNet

Wide ResNet

Wide Residual Networks are a variant on ResNets where we decrease depth and increase the width of residual networks. This is achieved through the use of wide residual blocks.

How do I use this model on an image?

To load a pretrained model:

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>>> import timm
>>> model = timm.create_model('wide_resnet101_2', pretrained=True)
>>> model.eval()

To load and preprocess the image:

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>>> 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:

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>>> 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:

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>>> # 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. wide_resnet101_2. 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, 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).

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>>> model = timm.create_model('wide_resnet101_2', 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 to use your dataset.

How do I train this model?

You can follow the timm recipe scripts for training a new model afresh.

Citation

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@article{DBLP:journals/corr/ZagoruykoK16,
  author    = {Sergey Zagoruyko and
               Nikos Komodakis},
  title     = {Wide Residual Networks},
  journal   = {CoRR},
  volume    = {abs/1605.07146},
  year      = {2016},
  url       = {http://arxiv.org/abs/1605.07146},
  archivePrefix = {arXiv},
  eprint    = {1605.07146},
  timestamp = {Mon, 13 Aug 2018 16:46:42 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/ZagoruykoK16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}