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Chen et al., 2021 — CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification (arXiv:2103.14899)
timm/crossvit_15_240.in1k,
converted to Lucid-native safetensors.| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
IN1K (default) | 81.5 | — | 27.5M | — | 105.05 MB | timm |
1import lucid.models as models
2from lucid.models.weights import CrossViT15Weights
3
4# default tag
5model = models.crossvit_15_cls(pretrained=True)
6
7# explicit tag (enum or string)
8model = models.crossvit_15_cls(weights=CrossViT15Weights.IN1K)
9model = models.crossvit_15_cls(pretrained="IN1K")
10
11# preprocessing travels with the weights
12weights = CrossViT15Weights.IN1K
13preprocess = weights.transforms()
14logits = model(preprocess(image)[None]).logitstimm/crossvit_15_240.in1k via
python -m tools.convert_weights crossvit_15 --tag IN1K.
Key mapping + numerical parity verified against the source.apache-2.0 — inherited from the original weights.@inproceedings{chen2021crossvit,
title={CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification},
author={Chen, Chun-Fu (Richard) and Fan, Quanfu and Panda, Rameswar},
booktitle={ICCV}, year={2021}
}