Deep Drowsiness Detection using YOLO, Pytorch and Python

Want to leverage YOLO object detection for safety? One great implementation is using it to determine when drivers might be feeling a little drowsy. In this video we’re going to do exactly that using a fine tuned, customer object detection model powered by YOLO and PyTorch! In this video you'll learn how to: 1. Instal Ultralytics YOLOv5 2. Detect Objects from Images 3. Detect Objects from Pre-Recorded Videos 4. Detect Objects in Real Time Using OpenCV 5. Fine Tuning a Drowsiness Model using YOLOv5 and PyTorch 6. Perform Real Time Drowsiness Detection Get the code: GitHub: https://github.com/nicknochnack/YOLO-Drowsiness-Detection Links Ultralytics YOLOv5: https://github.com/ultralytics/yolov5 PyTorch Installation: https://pytorch.org/get-started/locally/ COCO Classes: https://gist.github.com/AruniRC/7b3dadd004da04c80198557db5da4bda LabelImg: https://github.com/tzutalin/labelImg Chapters 0:00 - Start 0:48 - Introduction 1:18 - Gameplan 2:23 - How it Works 3:05 - Tutorial Start 4:12 - 1. Install and Import Dependencies 10:51 - 2. Load Model 13:44 - 3. Make Detections using Images 21:05 - 4. Real Time Detections and Object Detection using Videos 30:05 - 5. Train a Custom YOLO Model 1:10:28 - 6. Detecting Drowsiness 1:17:58 - Ending Oh, and don't forget to connect with me! LinkedIn: https://bit.ly/324Epgo Facebook: https://bit.ly/3mB1sZD GitHub: https://bit.ly/3mDJllD Patreon: https://bit.ly/2OCn3UW Join the Discussion on Discord: https://bit.ly/3dQiZsV Happy coding! Nick P.s. Let me know how you go and drop a comment if you need a hand!