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Zagoruyko & Komodakis, 2016 — Wide Residual Networks (arXiv:1605.07146)
torchvision/Wide_ResNet101_2_Weights.IMAGENET1K_V1,
converted to Lucid-native safetensors.| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
IMAGENET1K_V1 (default) | 78.848 | 94.284 | 126.9M | 22.753 | 484.62 MB | torchvision |
1import lucid.models as models
2from lucid.models.weights import WideResNet101Weights
3
4# default tag
5model = models.wide_resnet_101_cls(pretrained=True)
6
7# explicit tag (enum or string)
8model = models.wide_resnet_101_cls(weights=WideResNet101Weights.IMAGENET1K_V1)
9model = models.wide_resnet_101_cls(pretrained="IMAGENET1K_V1")
10
11# preprocessing travels with the weights
12weights = WideResNet101Weights.IMAGENET1K_V1
13preprocess = weights.transforms()
14logits = model(preprocess(image)[None]).logitstorchvision/Wide_ResNet101_2_Weights.IMAGENET1K_V1 via
python -m tools.convert_weights wide_resnet_101 --tag IMAGENET1K_V1.
Key mapping + numerical parity verified against the source.bsd-3-clause — inherited from the original weights.@inproceedings{zagoruyko2016wide,
title={Wide Residual Networks},
author={Zagoruyko, Sergey and Komodakis, Nikos},
booktitle={BMVC}, year={2016}
}