Explore the OpenCV DNN module and its applications in computer vision with GPU acceleration through this comprehensive tutorial series. Learn to deploy neural networks for high-speed object detection, implement object classification using TensorFlow, and perform monocular camera depth estimation in both Python and C++. Discover techniques for distance estimation to faces, create point clouds using deep learning, and master YOLOv5 custom object detection. Gain hands-on experience with practical examples and Python code for deploying various neural network models, including custom YOLOv5, using OpenCV.
Overview
Syllabus
Introduction to OpenCV DNN Module - OpenCV and Computer Vision with GPU.
How To Deploy Neural Networks with OpenCV DNN and GPU in Python | 100+ FPS Object Detection.
How To Deploy Neural Networks with OpenCV DNN and GPU in C++ | 100+ FPS Object Detection.
Deploy Neural Networks for Object Classification in OpenCV Python with TensorFlow.
Monocular Camera Depth Estimation with Neural Networks in OpenCV C++.
Python Monocular Camera Depth Estimation with Neural Networks in OpenCV.
Distance Estimation to Faces with a Monocular Camera using OpenCV Python and Neural Networks.
How To Create Point Clouds with Deep Learning and Neural Networks in OpenCV Python.
YOLOv5 Custom Object Detection with Code and Dataset - Neural Networks and Deep Learning.
How To Deploy YOLOv5 Object Detection Model with OpenCV - With Example and Python Code.
How To Deploy Custom YOLOv5 Model for Object Detection with OpenCV - With Example and Python Code.
Taught by
The Coding Lib
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