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
- Xet hash:
- d833dfe2a4d8c480b0f879f0963bd054102fb389a662e4ea29bbbd0f66bce063
- Size of remote file:
- 4.92 GB
- SHA256:
- dcc9305cc66c30db7ee74e0ffe8fdc356e1f7475eaf7eb8197617d5e53c146ac
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