timojl/clipseg

Health v2 · maintenance only

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1338 stars · 121 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

CLIPSeg is a Python implementation of the CVPR 2022 paper 'Image Segmentation Using Text and Image Prompts', enabling zero-shot segmentation of images based on arbitrary text queries or prompt images with masks. It provides pre-trained model weights and has been integrated into the HuggingFace Transformers library.

Use cases

• segment objects in an image using a text prompt
• zero-shot image segmentation without training
• create segmentation masks from a reference image with a highlighted region
• run text-guided segmentation in a notebook
• fine-grained segmentation predictions with pre-trained weights

When to choose

• you need to segment arbitrary objects described by text without training a model
• you want lightweight pre-trained weights for zero-shot segmentation
• you prefer a model already supported in HuggingFace Transformers

When to avoid

• you need fully supervised, dataset-specific segmentation with maximum accuracy
• you require a permissive license for the model weights (weights have a custom license)
• you need real-time segmentation on CPU-only hardware

Facets

library · maturity maintenance

machine-learning computer-vision image-processing deep-learning computer-vision image-processing machine-learning deep-learning python image-segmentation zero-shot clip text-prompted-segmentation cvpr-2022 huggingface-transformers gpu

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