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Szegedy et al., 2015 — Going Deeper with Convolutions (arXiv:1409.4842)
torchvision/GoogLeNet_Weights.IMAGENET1K_V1,
converted to Lucid-native safetensors.| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
IMAGENET1K_V1 (default) | 69.778 | 89.53 | 13.0M | 1.498 | 49.71 MB | torchvision |
1import lucid.models as models
2from lucid.models.weights import GoogLeNetWeights
3
4# default tag
5model = models.googlenet_cls(pretrained=True)
6
7# explicit tag (enum or string)
8model = models.googlenet_cls(weights=GoogLeNetWeights.IMAGENET1K_V1)
9model = models.googlenet_cls(pretrained="IMAGENET1K_V1")
10
11# preprocessing travels with the weights
12weights = GoogLeNetWeights.IMAGENET1K_V1
13preprocess = weights.transforms()
14logits = model(preprocess(image)[None]).logitstorchvision/GoogLeNet_Weights.IMAGENET1K_V1 via
python -m tools.convert_weights googlenet --tag IMAGENET1K_V1.
Key mapping + numerical parity verified against the source.bsd-3-clause — inherited from the original weights.@inproceedings{szegedy2015going,
title={Going Deeper with Convolutions},
author={Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew},
booktitle={CVPR}, pages={1--9}, year={2015}
}