Image Segmentation
Transformers
PyTorch
English
setcon_chat
feature-extraction
referring-segmentation
video-segmentation
vision-language
custom_code
Instructions to use rookiexiong/SetCon-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rookiexiong/SetCon-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="rookiexiong/SetCon-8B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rookiexiong/SetCon-8B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
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---
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license: apache-2.0
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language:
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- en
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library_name: transformers
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pipeline_tag: image-segmentation
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tags:
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- referring-segmentation
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- image-segmentation
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- video-segmentation
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- vision-language
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---
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# SetCon-8B
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SetCon-8B is the model checkpoint for **SetCon: Towards Open-Ended Referring Segmentation via Set-Level Concept Prediction**.
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## Usage
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Please use this checkpoint together with the official codebase:
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```bash
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git clone https://github.com/rookiexiong7/SetCon.git
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cd SetCon
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uv sync --extra latest
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source .venv/bin/activate
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```
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Single-image inference:
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```
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python demo.py \
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--image-path assets/room.jpg \
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--query-text "the target objects" \
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--model-path path/to/SetCon-8B
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```
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## Intended Use
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This model is intended for research on open-ended referring image/video segmentation.
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## Limitations
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The model may produce incomplete or inaccurate masks for ambiguous expressions, small objects, crowded scenes, or out-of-domain visual
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concepts.
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## Citation
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