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title: CLIPSegmentation
emoji: 🦀
colorFrom: red
colorTo: indigo
sdk: gradio
sdk_version: 5.25.2
app_file: app.py
pinned: false
license: mit
---
# CLIP Segmentation
CLIP Segmentation Project leverages the power of OpenAI's CLIP model combined with a segmentation decoder to perform image segmentation based on textual prompts. Provide an image and a text prompt, and get segmented masks for each prompt.
## Features
- **Textual Prompt Segmentation**: Segment images based on textual prompts.
- **Multiple Prompts**: Support for multiple prompts separated by commas.
- **Interactive UI**: User-friendly interface for easy image uploads and prompt inputs.
## Usage
1. Upload an image using the provided interface.
2. Enter your text prompts separated by commas.
3. Click on "Visualize Segments" to get the segmented masks.
4. Hover over a class to view the individual segment.
## How It Works
The CLIP Segmentation Project combines the power of a pretrained CLIP model with a segmentation decoder. The CLIP model, developed by OpenAI, understands images paired with natural language. By combining this with a segmentation decoder, we can generate segmented masks for images based on textual prompts, bridging the gap between vision and language in a unique way.
## Acknowledgements
- Thanks to [OpenAI](https://openai.com/) for the CLIP model.
- Thanks to [Image Segmentation Using Text and Image Prompts](https://github.com/timojl/clipseg). |