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Simonyan & Zisserman, 2015 — Very Deep Convolutional Networks for Large-Scale Image Recognition (arXiv:1409.1556)
torchvision/VGG16_BN_Weights.IMAGENET1K_V1,
converted to Lucid-native safetensors.| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
IMAGENET1K_V1 (default) | 73.36 | 91.516 | 138.4M | 15.47 | 527.87 MB | torchvision |
1import lucid.models as models
2from lucid.models.weights import VGG16BNWeights
3
4# default tag
5model = models.vgg_16_bn_cls(pretrained=True)
6
7# explicit tag (enum or string)
8model = models.vgg_16_bn_cls(weights=VGG16BNWeights.IMAGENET1K_V1)
9model = models.vgg_16_bn_cls(pretrained="IMAGENET1K_V1")
10
11# preprocessing travels with the weights
12weights = VGG16BNWeights.IMAGENET1K_V1
13preprocess = weights.transforms()
14logits = model(preprocess(image)[None]).logitstorchvision/VGG16_BN_Weights.IMAGENET1K_V1 via
python -m tools.convert_weights vgg_16_bn --tag IMAGENET1K_V1.
Key mapping + numerical parity verified against the source.bsd-3-clause — inherited from the original weights.@inproceedings{simonyan2015very,
title={Very Deep Convolutional Networks for Large-Scale Image Recognition},
author={Simonyan, Karen and Zisserman, Andrew},
booktitle={ICLR}, year={2015}
}