Image Segmentation
Transformers
Safetensors
English
internvl_chat
Visual Grounding
Referring Expression Segmentation
Generalized Referring Expression Segmentation
Referring Expression Comprehension
custom_code
Instructions to use jcwang0602/MLLMSeg_InternVL2_5_8B_RES with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcwang0602/MLLMSeg_InternVL2_5_8B_RES with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="jcwang0602/MLLMSeg_InternVL2_5_8B_RES", trust_remote_code=True)# Load model directly from transformers import MLLMSeg model = MLLMSeg.from_pretrained("jcwang0602/MLLMSeg_InternVL2_5_8B_RES", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add model card content and `transformers` library tag
#1
by nielsr HF Staff - opened
Hi! I've prepared a pull request to enhance the model card for jcwang0602/MLLMSeg_InternVL2_5_8B_RES.
This PR:
- Adds the
library_name: transformerstag to the metadata, which helps with discoverability and enables the "Use in Transformers" button on the model page. - Provides a comprehensive overview of the model, including the paper abstract, to explain its purpose and methodology.
- Integrates a detailed "Quick Start" section with Python code for easy inference, along with necessary preprocessing helper functions.
- Includes performance metrics and illustrative visualizations directly from the official GitHub repository to showcase the model's capabilities.
- Adds the official BibTeX citation for the paper.
Please review the changes and let me know if any adjustments are needed. Thanks!
jcwang0602 changed pull request status to merged