metadata
library_name: lucid
license: mit
tags:
- image-classification
- swin
- lucid
datasets:
- imagenet-1k
pipeline_tag: image-classification
model-index:
- name: swin-tiny
results:
- task:
type: image-classification
dataset:
name: ImageNet-1K
type: imagenet-1k
metrics:
- type: acc@1
value: 81.474
- type: acc@5
value: 95.776
Swin-Tiny
Liu et al., 2021 — Swin Transformer: Hierarchical Vision Transformer using Shifted Windows (arXiv:2103.14030)
Lucid port of torchvision/Swin_T_Weights.IMAGENET1K_V1,
converted to Lucid-native safetensors.
Available weights
| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
IMAGENET1K_V1 (default) |
81.474 | 95.776 | 28.3M | 4.491 | 107.93 MB | torchvision |
Usage
import lucid.models as models
from lucid.models.weights import SwinTinyWeights
# default tag
model = models.swin_tiny_cls(pretrained=True)
# explicit tag (enum or string)
model = models.swin_tiny_cls(weights=SwinTinyWeights.IMAGENET1K_V1)
model = models.swin_tiny_cls(pretrained="IMAGENET1K_V1")
# preprocessing travels with the weights
weights = SwinTinyWeights.IMAGENET1K_V1
preprocess = weights.transforms()
logits = model(preprocess(image)[None]).logits
Conversion
Converted from torchvision/Swin_T_Weights.IMAGENET1K_V1 via
python -m tools.convert_weights swin_tiny --tag IMAGENET1K_V1.
Key mapping + numerical parity verified against the source.
License
mit — inherited from the original weights.
Citation
@inproceedings{liu2021swin,
title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
booktitle={ICCV}, year={2021}
}