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: github.com/nicknochnack/YOLO-...
Links
Ultralytics YOLOv5: github.com/ultralytics/yolov5
PyTorch Installation: pytorch.org/get-started/locally/
COCO Classes: gist.github.com/AruniRC/7b3da...
LabelImg: 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: bit.ly/324Epgo
Facebook: bit.ly/3mB1sZD
GitHub: bit.ly/3mDJllD
Patreon: bit.ly/2OCn3UW
Join the Discussion on Discord: bit.ly/3dQiZsV
Happy coding!
Nick
P.s. Let me know how you go and drop a comment if you need a hand!

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