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| title: Image Search with ColModernVBert | |
| emoji: π | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: "4.44.0" | |
| app_file: app.py | |
| pinned: false | |
| # Image Search with ColModernVBert | |
| A multimodal image search demo using ColModernVBert for cross-modal retrieval between text queries and images. | |
| ## Features | |
| - **Multimodal Search**: Query images using natural language text | |
| - **ImageNet-1K Dataset**: Searches through 1000 diverse validation images | |
| - **Real-time Indexing**: Automatically indexes 1000 images on startup | |
| - **512x512 Optimization**: Images resized for optimal model performance | |
| - **Multiprocessing**: Fast parallel image preprocessing | |
| - **Upload Support**: Upload custom images or switch datasets | |
| ## Usage | |
| Enter text queries like: | |
| - `"dog"` β Various dog breeds | |
| - `"sports car"` β Different car models | |
| - `"musical instrument"` β Guitars, pianos, violins | |
| - `"food"` β Fruits, vegetables, dishes | |
| - `"nature"` β Trees, flowers, landscapes | |
| Adjust the Top-K slider to control the number of results returned. | |
| ## Technical Details | |
| - **Model**: ColModernVBert (ModernVBERT/colmodernvbert) | |
| - **Dataset**: ImageNet-1K validation set (1000 images) | |
| - **Image Size**: 512x512 pixels | |
| - **Embeddings**: Cosine similarity between text and image embeddings | |
| ## Performance | |
| - **Indexing**: ~2-3 minutes for 1000 images | |
| - **Search**: Near-instant results | |
| - **Memory**: Optimized for Space hardware limits | |
| --- | |
| Built with Gradio and ColModernVBert for demonstration purposes. | |