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!

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