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
Add model card metadata and description
#1
by nielsr HF Staff - opened
Hi, 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-extractionpipeline tag for better discoverability. - Adds
library_name: transformersbased 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!
fushh7 changed pull request status to merged