Learn how to implement a deep drowsiness detection system using YOLO, PyTorch, and Python in this comprehensive tutorial video. Discover the process of leveraging YOLO object detection for driver safety by creating a fine-tuned, custom object detection model. Follow along to install Ultralytics YOLOv5, detect objects from images and pre-recorded videos, perform real-time object detection using OpenCV, fine-tune a drowsiness model with YOLOv5 and PyTorch, and implement real-time drowsiness detection. Gain hands-on experience with step-by-step instructions, from setting up dependencies to training a custom YOLO model and applying it to detect driver drowsiness. Access provided resources, including GitHub code repository, PyTorch installation guide, and additional tools like LabelImg for efficient implementation.
Deep Drowsiness Detection Using YOLO, Pytorch and Python
Overview
Syllabus
- Start
- Introduction
- Gameplan
- How it Works
- Tutorial Start
- 1. Install and Import Dependencies
- 2. Load Model
- 3. Make Detections using Images
- 4. Real Time Detections and Object Detection using Videos
- 5. Train a Custom YOLO Model
- 6. Detecting Drowsiness
- Ending
Taught by
Nicholas Renotte
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