YOLOv8 Performance Improvement Masterclass

Architecture Modification, OpenVino+Quantization, TensorRT, Hyperparameter Tuning, Augmentation,Pseudo-Labeling,on COLAB

Created by: Dr. Priyanto Hidayatullah (Ph.D in AI)
Ph.D in Artificial Intelligence

Created by: Stunning Vision AI Academy
Providing high-quality education in AI and Computer Vision.

What Will I Learn?

• Modify YOLOv8 Architecture to improve object detection speed and accuracy
• Boost YOLO v8 Speed in CPU mode with OpenVino and Model Quantization
• Improve YOLOv8 Speed in GPU mode using TensorRT Optimization
• Tune Hyperparameter
• Perform Data Augmentation Techniques That Suit Your Dataset
• Add data semi-automatically with Pseudo-Labeling
• Deeply Understand YOLOv8 Architecture
• Perform YOLOv8 Installation and its dependencies
• Understand how Convolutional Neural Network works

Requirements

• Convolutional Neural Network (CNN) understanding is beneficial, but not mandatory. We will explain CNN in detail.

Target audience

• Deep Learning - Computer Vision Professionals interested in BOOSTING YOLOv8 performance
• Students who are doing final projects and writing thesis/dissertation
• Researchers who are searching new ideas
• Deep Learning - Computer Vision Enthusiast