This course covers deploying neural networks with OpenCV DNN and GPU for object detection and classification. Students will learn to create point clouds, estimate distances, and work with YOLOv5 for custom object detection. The teaching method includes hands-on examples in Python and C++. The intended audience for this course includes computer vision enthusiasts, machine learning practitioners, and developers looking to enhance their skills in deep learning and neural networks.
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
