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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, WeDetectEmbedding processor = AutoProcessor.from_pretrained("fushh7/ObjEmbed-2B") model = WeDetectEmbedding.from_pretrained("fushh7/ObjEmbed-2B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add model card metadata and description
Verified signatureHi, I'm Niels from the Hugging Face community science team. I'm opening this PR to improve your model card with relevant metadata and a descriptive summary of your work. This update: - Adds the `image-feature-extraction` pipeline tag for better discoverability. - Adds `library_name: transformers` based on the configuration files and requirements. - Links the model card to the official paper and GitHub repository. - Includes a brief overview of ObjEmbed's key properties. Feel free to merge this if it looks good!a65f226
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Update README.md
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Upload folder using huggingface_hub
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initial commit
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