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This project implements an object detection system using the YOLOv5 (You Only Look Once) model. YOLOv5 is a state-of-the-art, real-time object detection algorithm that is both fast and accurate. This system can detect multiple objects in images or video streams and can be further fine-tuned for custom datasets. It includes training the YOLOv5 model, evaluating it on a test dataset, and running real-time inference.
Describe the solution you'd like Real-time object detection on images and video streams. Training the YOLOv5 model on custom datasets. Evaluation using key metrics such as Precision, Recall, Intersection over Union (IoU), and Mean Average Precision (mAP). Deployment for detecting objects in images and video streams (GPU requirements for this case is much preferrable) Model robustness testing with image augmentations.
Requirements: Python 3.7+ PyTorch 1.7+ YOLOv5 (via the ultralytics/yolov5 repository) Common libraries: numpy opencv-python torch pillow matplotlib albumentations
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