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Li et al., 2022 — EfficientFormer: Vision Transformers at MobileNet Speed (arXiv:2206.01191)
timm/efficientformer_l1.snap_dist_in1k,
converted to Lucid-native safetensors.| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
SNAP_DIST_IN1K (default) | 79.2 | — | 12.3M | — | 47.03 MB | timm |
1import lucid.models as models
2from lucid.models.weights import EfficientFormerL1Weights
3
4# default tag
5model = models.efficientformer_l1_cls(pretrained=True)
6
7# explicit tag (enum or string)
8model = models.efficientformer_l1_cls(weights=EfficientFormerL1Weights.SNAP_DIST_IN1K)
9model = models.efficientformer_l1_cls(pretrained="SNAP_DIST_IN1K")
10
11# preprocessing travels with the weights
12weights = EfficientFormerL1Weights.SNAP_DIST_IN1K
13preprocess = weights.transforms()
14logits = model(preprocess(image)[None]).logitstimm/efficientformer_l1.snap_dist_in1k via
python -m tools.convert_weights efficientformer_l1 --tag SNAP_DIST_IN1K.
Key mapping + numerical parity verified against the source.apache-2.0 — inherited from the original weights.@article{li2022efficientformer,
title={EfficientFormer: Vision Transformers at MobileNet Speed},
author={Li, Yanyu and Yuan, Geng and Wen, Yang and Hu, Eric and Evangelidis, Georgios and Tulyakov, Sergey and Wang, Yanzhi and Ren, Jian},
journal={Advances in Neural Information Processing Systems},
volume={35}, year={2022}
}