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Wang et al., 2022 — PVT v2: Improved Baselines with Pyramid Vision Transformer (arXiv:2106.13797)
timm/pvt_v2_b5.in1k,
converted to Lucid-native safetensors.| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
IN1K (default) | 83.8 | — | 82.0M | — | 312.74 MB | timm |
1import lucid.models as models
2from lucid.models.weights import PVTv2B5Weights
3
4# default tag
5model = models.pvt_v2_b5_cls(pretrained=True)
6
7# explicit tag (enum or string)
8model = models.pvt_v2_b5_cls(weights=PVTv2B5Weights.IN1K)
9model = models.pvt_v2_b5_cls(pretrained="IN1K")
10
11# preprocessing travels with the weights
12weights = PVTv2B5Weights.IN1K
13preprocess = weights.transforms()
14logits = model(preprocess(image)[None]).logitstimm/pvt_v2_b5.in1k via
python -m tools.convert_weights pvt_v2_b5 --tag IN1K.
Key mapping + numerical parity verified against the source.apache-2.0 — inherited from the original weights.@article{wang2022pvtv2,
title={PVT v2: Improved Baselines with Pyramid Vision Transformer},
author={Wang, Wenhai and Xie, Enze and Li, Xiang and Fan, Deng-Ping and Song, Kaitao and Liang, Ding and Lu, Tong and Luo, Ping and Shao, Ling},
journal={Computational Visual Media}, year={2022}
}