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Huang et al., 2017 — Densely Connected Convolutional Networks (arXiv:1608.06993)
torchvision/DenseNet201_Weights.IMAGENET1K_V1,
converted to Lucid-native safetensors.| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
IMAGENET1K_V1 (default) | 76.896 | 93.37 | 20.0M | 4.291 | 77.37 MB | torchvision |
1import lucid.models as models
2from lucid.models.weights import DenseNet201Weights
3
4# default tag
5model = models.densenet_201_cls(pretrained=True)
6
7# explicit tag (enum or string)
8model = models.densenet_201_cls(weights=DenseNet201Weights.IMAGENET1K_V1)
9model = models.densenet_201_cls(pretrained="IMAGENET1K_V1")
10
11# preprocessing travels with the weights
12weights = DenseNet201Weights.IMAGENET1K_V1
13preprocess = weights.transforms()
14logits = model(preprocess(image)[None]).logitstorchvision/DenseNet201_Weights.IMAGENET1K_V1 via
python -m tools.convert_weights densenet_201 --tag IMAGENET1K_V1.
Key mapping + numerical parity verified against the source.bsd-3-clause — inherited from the original weights.@inproceedings{huang2017densely,
title={Densely Connected Convolutional Networks},
author={Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q.},
booktitle={CVPR}, year={2017}
}