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lucid-dl/sk-resnet-34
sk-resnet-34 is a image classification model from lucid-dl. Use it when you need a label for an image. It is set up for lucid. The card lists the license as apache-2.0.
Li et al., 2019 — Selective Kernel Networks (arXiv:1903.06586)
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Updated May 29, 2026
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From the Hugging Face model README
Li et al., 2019 — Selective Kernel Networks (arXiv:1903.06586)
Lucid port of timm/skresnet34.ra_in1k,
converted to Lucid-native safetensors.
| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
RA_IN1K (default) | 76.956 | — | 22.3M | — | 85.14 MB | timm |
import lucid.models as models
from lucid.models.weights import SKResNet34Weights
# default tag
model = models.sk_resnet_34_cls(pretrained=True)
# explicit tag (enum or string)
model = models.sk_resnet_34_cls(weights=SKResNet34Weights.RA_IN1K)
model = models.sk_resnet_34_cls(pretrained="RA_IN1K")
# preprocessing travels with the weights
weights = SKResNet34Weights.RA_IN1K
preprocess = weights.transforms()
logits = model(preprocess(image)[None]).logits
Converted from timm/skresnet34.ra_in1k via
python -m tools.convert_weights sk_resnet_34 --tag RA_IN1K.
Key mapping + numerical parity verified against the source.
apache-2.0 — inherited from the original weights.
@inproceedings{li2019selective,
title={Selective Kernel Networks},
author={Li, Xiang and Wang, Wenhai and Hu, Xiaolin and Yang, Jian},
booktitle={CVPR}, year={2019}
}