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Tan & Le, 2019 — EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (arXiv:1905.11946)
torchvision/EfficientNet_B5_Weights.IMAGENET1K_V1,
converted to Lucid-native safetensors.| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
IMAGENET1K_V1 (default) | 83.444 | 96.628 | 30.4M | 10.266 | 116.67 MB | torchvision |
1import lucid.models as models
2from lucid.models.weights import EfficientNetB5Weights
3
4# default tag
5model = models.efficientnet_b5_cls(pretrained=True)
6
7# explicit tag (enum or string)
8model = models.efficientnet_b5_cls(weights=EfficientNetB5Weights.IMAGENET1K_V1)
9model = models.efficientnet_b5_cls(pretrained="IMAGENET1K_V1")
10
11# preprocessing travels with the weights
12weights = EfficientNetB5Weights.IMAGENET1K_V1
13preprocess = weights.transforms()
14logits = model(preprocess(image)[None]).logitstorchvision/EfficientNet_B5_Weights.IMAGENET1K_V1 via
python -m tools.convert_weights efficientnet_b5 --tag IMAGENET1K_V1.
Key mapping + numerical parity verified against the source.apache-2.0 — inherited from the original weights.@inproceedings{tan2019efficientnet,
title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
author={Tan, Mingxing and Le, Quoc},
booktitle={ICML}, year={2019}
}