JRYOLO - Computer Vision Application
A comprehensive web application for image and video processing using YOLO (You Only Look Once) models, providing a user-friendly interface for various computer vision tasks.
Object Detection
Upload images to identify and locate objects with adjustable confidence thresholds and bounding box visualization. Supports processing images from URLs.
Image Segmentation
Perform pixel-level semantic segmentation of objects in images with transparent colored masks visualization.
Pose Estimation
Detect human body keypoints in images and visualize skeletons and joints on detected persons.
Video Analysis
Process uploaded video files with detection, segmentation, and pose estimation capabilities. Includes real-time webcam streaming.
Model Training
Complete interface for training custom YOLO models with dataset upload, automatic structure verification, and real-time training progress tracking.
Model Management
Detailed visualization of model classes, performance metrics, and access to training artifacts and visualizations.
Technologies
- Python/Flask for backend processing
- HTML/CSS/JavaScript for frontend interfaces
- YOLO (various versions) for object detection
- OpenCV for image processing
- PyTorch for deep learning operations
- RESTful API architecture
- Asynchronous processing for long operations
- Hugging Face integration for model hosting
Implementation
Key technical aspects of this project:
- Modular architecture with specialized utility components
- Robust YAML handling with common error correction
- Model information extraction from multiple sources
- Metrics management with multi-location search capability
- Asynchronous processing for non-blocking interfaces
- Comprehensive error handling and validation
Key Features
The application supports multiple formats and operations:
- Multiple Format Support:
- Images: PNG, JPG, JPEG, GIF
- Videos: MP4, AVI
- Datasets: ZIP
- RESTful API:
- Endpoints for all main functionalities
- JSON responses for integration with other applications
- Asynchronous Processing:
- Training in separate threads to avoid blocking the interface
- Real-time monitoring of long operation progress
- Organized Storage:
- Folder structure for uploads, results, models, and datasets
- Unique file naming generation
The application is designed to be accessible for users with limited technical knowledge while offering powerful tools for computer vision professionals.
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