Instructions to use sthui/SimpleSeg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sthui/SimpleSeg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sthui/SimpleSeg", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sthui/SimpleSeg", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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license: mit
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library_name: transformers
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# Towards Pixel-level VLM Perception via Simple Points Prediction
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<div align="center">
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license: mit
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library_name: transformers
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---
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> [!Note]
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> This checkpoint was deprecated. Please consider using the latest versions: [SimpleSeg-Kimi-VL](https://huggingface.co/sthui/SimpleSeg-Kimi-VL) and [SimpleSeg-Qwen2.5-VL](https://huggingface.co/sthui/SimpleSeg-Qwen2.5-VL).
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# Towards Pixel-level VLM Perception via Simple Points Prediction
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<div align="center">
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