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Chollet, 2017 — Xception: Deep Learning with Depthwise Separable Convolutions (arXiv:1610.02357)
timm/legacy_xception.tf_in1k,
converted to Lucid-native safetensors.| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
TF_IN1K (default) | 79.0 | — | 22.9M | — | 87.42 MB | timm |
1import lucid.models as models
2from lucid.models.weights import XceptionWeights
3
4# default tag
5model = models.xception_cls(pretrained=True)
6
7# explicit tag (enum or string)
8model = models.xception_cls(weights=XceptionWeights.TF_IN1K)
9model = models.xception_cls(pretrained="TF_IN1K")
10
11# preprocessing travels with the weights
12weights = XceptionWeights.TF_IN1K
13preprocess = weights.transforms()
14logits = model(preprocess(image)[None]).logitstimm/legacy_xception.tf_in1k via
python -m tools.convert_weights xception --tag TF_IN1K.
Key mapping + numerical parity verified against the source.apache-2.0 — inherited from the original weights.@inproceedings{chollet2017xception,
title={Xception: Deep Learning with Depthwise Separable Convolutions},
author={Chollet, Fran\c{c}ois},
booktitle={CVPR}, year={2017}
}