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It processes a video stream, detects objects, collects training data, and simultaneously displays object counts and neural network training progress.\r\n\r\n\u003ch1 align=\"center\"\u003e\r\n\u003cimg src=\"demo1.png\"\u003e\r\n\u003cimg src=\"demo2.png\"\u003e\r\n\u003cimg src=\"demo3.png\"\u003e\r\n\u003cimg src=\"demo4.png\"\u003e\r\n\u003c/h1\u003e\r\n\r\n## Features\r\n- Real-time object detection using YOLOv8\r\n- Concurrent neural network training on detected objects\r\n- Live visualization of object counts and training loss\r\n- Multithreaded processing for smooth performance\r\n\r\n## Requirements\r\n- Python 3.7+\r\n- OpenCV\r\n- NumPy\r\n- Matplotlib\r\n- Ultralytics YOLO\r\n- TensorFlow/Keras\r\n\r\n````\r\npip install -r requirements.txt\r\n````\r\n\r\n\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fivangael%2Fobjects_detection_with_neural_network_training_visualization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fivangael%2Fobjects_detection_with_neural_network_training_visualization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fivangael%2Fobjects_detection_with_neural_network_training_visualization/lists"}