Image Segmentation
Transformers
Safetensors
multilingual
sa2va_chat
image-feature-extraction
Sa2VA
custom_code
Instructions to use kumuji/Sa2VA-i-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kumuji/Sa2VA-i-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="kumuji/Sa2VA-i-1B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kumuji/Sa2VA-i-1B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card: update pipeline tag, add paper/code links, and expand description
#1
by nielsr HF Staff - opened
This PR significantly enhances the model card for this Sa2VA-i model by:
- Updating the
pipeline_tagtoimage-segmentation. This change more accurately reflects the model's core functionality of language-guided dense grounding and referring video object segmentation, ensuring better discoverability on the Hugging Face Hub. - Adding explicit links to the Hugging Face paper page (Sa2VA-i: Improving Sa2VA Results with Consistent Training and Inference) and the official GitHub repository (
https://github.com/kumuji/sa2va-i). - Expanding the model description with a detailed overview, performance highlights, competition results, model zoo, quick start guidance, and key improvements, all sourced directly from the project's GitHub README. This provides comprehensive information to users.
- Including authors and a teaser image for better presentation.
- Consolidating citations to include both the Sa2VA-i paper and the original Sa2VA paper.
- Removing irrelevant "File information" from the public model card.
These updates aim to provide a more complete, accurate, and user-friendly model card.
Awesome! Thank you so much!
kumuji changed pull request status to merged