Image Feature Extraction
Transformers
Safetensors
English
gr_lite
fashion
image-retrieval
vision-transformer
dino
custom_code
Instructions to use srpone/gr-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use srpone/gr-lite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="srpone/gr-lite", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("srpone/gr-lite", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -28,7 +28,7 @@ GR-Lite achieves state-of-the-art performance on [LookBench](https://huggingface
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| Framework | PyTorch / Transformers |
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Update: The pro version can be accessed via **https://apiclaw.io/en/api-docs** with /openapi/v2/model/fashion-image-embedding endpoint.
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```bibtex
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@article{gao2026lookbench,
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title={LookBench: A Live and Holistic Open Benchmark for Fashion Image Retrieval},
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author={Chao Gao and Siqiao Xue and
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year={2026},
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url={https://arxiv.org/abs/2601.14706},
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journal={arXiv preprint arXiv:2601.14706},
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** Update: The pro version can be accessed via **https://apiclaw.io/en/api-docs** with /openapi/v2/model/fashion-image-embedding endpoint. **
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```bibtex
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@article{gao2026lookbench,
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title={LookBench: A Live and Holistic Open Benchmark for Fashion Image Retrieval},
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author={Chao Gao and Siqiao Xue and Jiwen Fu and Tingyi Gu and Shanshan Li and Fan Zhou},
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year={2026},
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url={https://arxiv.org/abs/2601.14706},
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journal={arXiv preprint arXiv:2601.14706},
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