Eduardo Ottone
ee92581211
- Implements YOLOv8-based object detection with batch output - Supports multiple YOLOv8 model sizes (nano to extra-large) - Configurable confidence threshold, IoU threshold, and padding - Outputs separate images for each detected object - Includes installation scripts for Windows, Linux, and Mac - Complete documentation and example workflow - MIT license for open source distribution
169 lines
5.6 KiB
Markdown
169 lines
5.6 KiB
Markdown
# ComfyUI YOLOv8 Object Detection Node
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[](https://opensource.org/licenses/MIT)
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[](https://www.python.org/downloads/)
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[](https://github.com/comfyanonymous/ComfyUI)
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A powerful ComfyUI custom node that performs object detection using YOLOv8 and outputs individual images for each detected object. Perfect for automatic mask generation, object isolation, and batch processing workflows.
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## ✨ Features
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- **Object Detection**: Uses YOLOv8 models for accurate object detection
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- **Multiple Outputs**: Generates separate images for each detected object
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- **Batch Processing**: Outputs a batch of images containing all detected objects
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- **Customizable Parameters**: Adjustable confidence threshold, IoU threshold, and padding
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- **Model Selection**: Choose from different YOLOv8 model sizes (nano to extra-large)
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- **Custom Models**: Support for custom trained YOLOv8 models
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## 🚀 Quick Start
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### Automatic Installation
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1. **Clone the repository** into your ComfyUI custom nodes directory:
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```bash
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cd ComfyUI/custom_nodes/
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git clone https://github.com/mangobyed/ComfyUI_Detection_List.git
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cd ComfyUI_Detection_List
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```
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2. **Run the installation script**:
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**Linux/Mac:**
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```bash
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chmod +x install.sh
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./install.sh
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```
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**Windows:**
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```cmd
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install.bat
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```
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3. **Restart ComfyUI** to load the new node.
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### Manual Installation
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1. **Install Dependencies**:
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```bash
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pip install -r requirements.txt
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```
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2. **Copy Files**: Place this folder in your ComfyUI `custom_nodes` directory:
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```
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ComfyUI/custom_nodes/ComfyUI_Detection_List/
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```
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3. **Restart ComfyUI**: Restart ComfyUI to load the new node.
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## Usage
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### Node Inputs
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- **image**: Input image for object detection
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- **model_name**: YOLOv8 model to use (yolov8n.pt, yolov8s.pt, yolov8m.pt, yolov8l.pt, yolov8x.pt)
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- **confidence_threshold**: Minimum confidence score for detections (0.0 - 1.0)
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- **iou_threshold**: IoU threshold for non-maximum suppression (0.0 - 1.0)
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- **padding**: Pixels to add around detected objects when cropping (0 - 100)
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- **custom_model_path** (optional): Path to custom YOLOv8 model file
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### Node Outputs
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- **detected_objects**: Batch of images containing cropped detected objects
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- **object_count**: Number of objects detected
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- **class_names**: Comma-separated list of detected object classes
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- **detection_info**: Detailed information about each detection
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### Example Workflow
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1. Load an image using an image loader node
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2. Connect the image to the YOLOv8 Object Detection node
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3. Adjust detection parameters as needed
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4. Connect the outputs to:
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- Image preview nodes to see detected objects
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- Save image nodes to export results
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- Other processing nodes for further manipulation
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## Model Information
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### Default Models
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- **yolov8n.pt**: Nano - Fastest, smallest, lowest accuracy
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- **yolov8s.pt**: Small - Good balance of speed and accuracy
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- **yolov8m.pt**: Medium - Better accuracy, moderate speed
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- **yolov8l.pt**: Large - High accuracy, slower
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- **yolov8x.pt**: Extra Large - Highest accuracy, slowest
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### Custom Models
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You can use custom trained YOLOv8 models by providing the path in the `custom_model_path` input.
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## Parameters Guide
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- **Confidence Threshold**: Lower values detect more objects but may include false positives
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- **IoU Threshold**: Controls overlap tolerance for duplicate detections
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- **Padding**: Adds context around detected objects in cropped images
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## Troubleshooting
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1. **Model Loading Issues**: Ensure ultralytics is properly installed
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2. **GPU Memory**: Use smaller models (nano/small) for limited GPU memory
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3. **No Detections**: Try lowering the confidence threshold
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4. **Too Many False Positives**: Increase the confidence threshold
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## Requirements
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- ComfyUI
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- PyTorch
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- ultralytics
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- OpenCV
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- Pillow
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- NumPy
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See `requirements.txt` for specific version requirements.
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## 🤝 Contributing
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Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
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### Development Setup
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1. Fork the repository
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2. Clone your fork: `git clone https://github.com/yourusername/ComfyUI_Detection_List.git`
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3. Create a feature branch: `git checkout -b feature/amazing-feature`
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4. Make your changes and test them
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5. Commit your changes: `git commit -m 'Add amazing feature'`
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6. Push to the branch: `git push origin feature/amazing-feature`
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7. Open a Pull Request
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## 📝 Changelog
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### v1.0.0
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- Initial release
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- YOLOv8 object detection support
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- Batch image output functionality
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- Configurable detection parameters
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- Support for custom models
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## 🐛 Known Issues
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- Model loading may take some time on first use
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- GPU memory usage depends on model size and image resolution
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- Some custom models may require additional dependencies
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## 💬 Support
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If you encounter any issues or have questions:
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1. Check the [Issues](https://github.com/mangobyed/ComfyUI_Detection_List/issues) page
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2. Create a new issue with detailed information about your problem
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3. Include your ComfyUI version, Python version, and error logs
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## 🙏 Acknowledgments
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- [Ultralytics](https://github.com/ultralytics/ultralytics) for the YOLOv8 implementation
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- [ComfyUI](https://github.com/comfyanonymous/ComfyUI) for the amazing framework
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- The open-source community for continuous support and contributions
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## 📄 License
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This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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**Note**: YOLOv8 models are subject to their respective licenses from Ultralytics. |