Image Feature Extraction
Transformers
Safetensors
qwen3_vl
multimodal
vision-language
embeddings
image-retrieval
visual-grounding
Instructions to use fushh7/ObjEmbed-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fushh7/ObjEmbed-2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="fushh7/ObjEmbed-2B")# Load model directly from transformers import AutoProcessor, WeDetectEmbedding processor = AutoProcessor.from_pretrained("fushh7/ObjEmbed-2B") model = WeDetectEmbedding.from_pretrained("fushh7/ObjEmbed-2B") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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license: apache-2.0
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---
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## ObjEmbed: Towards Universal Multimodal Object Embeddings
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This is the official PyTorch implementation of [ObjEmbed](https://arxiv.org/abs/2602.01753). Please see our [GitHub](https://github.com/WeChatCV/ObjEmbed).
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If you find our work helpful for your research, please consider citing our paper.
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```
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@article{fu2026objembed,
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title={ObjEmbed: Towards Universal Multimodal Object Embeddings},
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author={Fu, Shenghao and Su, Yukun and Rao, Fengyun and LYU, Jing and Xie, Xiaohua and Zheng, Wei-Shi},
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journal={arXiv preprint arXiv:2602.01753},
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year={2026}
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}
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```
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