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--- |
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title: SAM3 Detection Browser |
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emoji: 🔍 |
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colorFrom: blue |
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colorTo: green |
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sdk: static |
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pinned: false |
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models: |
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- facebook/sam3 |
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--- |
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# 🔍 SAM3 Object Detection Browser |
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A simple, interactive web application for browsing and exploring object detection results from Meta's [SAM3 (Segment Anything Model 3)](https://huggingface.co/facebook/sam3). |
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## Features |
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- **Visual Detection Display**: View images with bounding boxes overlaid on detected objects |
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- **Confidence Filtering**: Adjust confidence threshold with a slider to filter detections |
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- **Statistics Dashboard**: See overall detection statistics including: |
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- Total filtered images |
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- Images with detections |
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- Total detections |
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- Average detections per image |
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- **Flexible Navigation**: Browse through datasets with pagination |
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- **Real-time Updates**: Filters update visualization instantly |
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- **Color-coded Confidence**: Bounding boxes and scores are color-coded: |
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- 🟢 Green: High confidence (≥70%) |
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- 🟠 Orange: Medium confidence (40-70%) |
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- 🔴 Red: Low confidence (<40%) |
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## How to Use |
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1. **Enter a Dataset ID**: Input any HuggingFace dataset that contains object detection results from SAM3 |
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- Default: `davanstrien/newspapers-image-predictions` |
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- Format: `username/dataset-name` |
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2. **Select Split**: Choose which dataset split to view (train/validation/test) |
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3. **Adjust Filters**: |
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- Move the confidence slider to filter detections by minimum confidence score |
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- Check "Show only images with detections" to hide images without any detected objects |
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4. **Browse**: Navigate through pages using Previous/Next buttons |
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## Dataset Requirements |
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This browser works with any HuggingFace dataset that has: |
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- An `image` column containing images |
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- An `objects` column with the structure: |
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```python |
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{ |
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"bbox": [[x, y, width, height], ...], # Bounding box coordinates |
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"category": [0, 0, ...], # Category indices |
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"score": [0.8, 0.6, ...] # Confidence scores |
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} |
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``` |
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This matches the output format from the [detect-objects.py](https://huggingface.co/datasets/uv-scripts/sam3/blob/main/detect-objects.py) script. |
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## Example Datasets |
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- [davanstrien/newspapers-image-predictions](https://huggingface.co/datasets/davanstrien/newspapers-image-predictions) - Photograph detections in historical newspapers |
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## Creating Your Own Detection Dataset |
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Use the SAM3 detection script to create your own datasets: |
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```bash |
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# Detect objects in your images |
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uv run https://huggingface.co/datasets/uv-scripts/sam3/raw/main/detect-objects.py \ |
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your-dataset \ |
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your-output-dataset \ |
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--class-name "photograph" \ |
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--confidence-threshold 0.5 |
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``` |
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Then view the results by entering `your-output-dataset` in this browser! |
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## Technical Details |
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- **Technology**: Pure HTML/CSS/JavaScript (no backend required) |
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- **Data Source**: HuggingFace Datasets API (parquet endpoint) |
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- **Rendering**: HTML5 Canvas for bounding box overlays |
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- **Performance**: Loads 12 images per page for optimal performance |
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## Related Projects |
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- [SAM3 Detection Script](https://huggingface.co/datasets/uv-scripts/sam3) - Create detection datasets |
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- [SAM3 Model](https://huggingface.co/facebook/sam3) - The base model |
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- [UV Scripts Organization](https://huggingface.co/uv-scripts) - More ready-to-run ML scripts |
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## License |
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MIT License - Feel free to use and modify for your own projects! |
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