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Real-Time-Social-Distancing-Detection-using-YOLO-Algorithm

Covid-19 has imposed individuals to follow social distancing to stop its spread and built a model that detects people who are violating it in public places using the YOLOv5 deep learning Algorithm. Trained the model for 300 epochs on the COCO dataset, performed transfer learning strategy with CNN to increase the model's performance, and was able to achieve an accuracy of 97.8%.

Execution of Project

1.Open file training.ipynp from the folder. 2.Run all the cells in the training.ipynp . 3.After Executing all the cells in training.ipynp the results of the training folder will be observed in yolov5/runs/train/exp. 4.Open detection.ipynp file from the Yolo folder. 5.Run all the cells in detection.ipynp .The input video is present in Yolo/storage/videos. 6.After executing all the cells in detection.ipynp file then output video will be reflected in Yolo/storage/output.

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