Feature request: Object Detection using COCO (refined) dataset with Advanced YOLO-v5 model

[Panchadip-128]

Is your feature request related to a problem? Please describe.

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

Describe alternatives you've considered
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Approach to be followed (optional)
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Additional context
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[github-actions]

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[github-actions]

Hello @Panchadip-128! Your issue #840 has been closed. Thank you for your contribution!