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Howard et al., 2017 — MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (arXiv:1704.04861)
timm/mobilenetv1_100.ra4_e3600_r224_in1k,
converted to Lucid-native safetensors.| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
RA4_E3600_R224_IN1K (default) | 0.0 | 0.0 | 4.2M | — | 16.24 MB | timm |
1import lucid.models as models
2from lucid.models.weights import MobileNetV1Weights
3
4# default tag
5model = models.mobilenet_v1_cls(pretrained=True)
6
7# explicit tag (enum or string)
8model = models.mobilenet_v1_cls(weights=MobileNetV1Weights.RA4_E3600_R224_IN1K)
9model = models.mobilenet_v1_cls(pretrained="RA4_E3600_R224_IN1K")
10
11# preprocessing travels with the weights
12weights = MobileNetV1Weights.RA4_E3600_R224_IN1K
13preprocess = weights.transforms()
14logits = model(preprocess(image)[None]).logitstimm/mobilenetv1_100.ra4_e3600_r224_in1k via
python -m tools.convert_weights mobilenet_v1 --tag RA4_E3600_R224_IN1K.
Key mapping + numerical parity verified against the source.apache-2.0 — inherited from the original weights.@article{howard2017mobilenets,
title={MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications},
author={Howard, Andrew G. and Zhu, Menglong and Chen, Bo and Kalenichenko, Dmitry and Wang, Weijun and Weyand, Tobias and Andreetto, Marco and Adam, Hartwig},
journal={arXiv preprint arXiv:1704.04861}, year={2017}
}