Computer Vision Systems with PyTorch & OpenCV

Course Overview

Develop computer vision software capable of processing digital images and real-time video feeds. This course focuses on fundamental image manipulations, convolutional neural networks, object detection architectures, and deploying models to local hardware environments.

What You Will Learn

• Process and transform digital image matrices using OpenCV and NumPy routines.
• Train Convolutional Neural Networks (CNNs) for image classification tasks in PyTorch.
• Implement real-time object detection models using YOLO and SSD frameworks.
• Perform image segmentation and feature extraction for automated visual inspection.

Syllabus Breakdown

• Module 1: Digital Image Fundamentals: Filtering, color spaces, edge detection, and matrix operations in OpenCV.
• Module 2: Convolutional Neural Networks: CNN architectures, transfer learning, data augmentation, and PyTorch setups.
• Module 3: Object Detection & Tracking: YOLO model training, bounding box evaluation, and real-time video tracking.
• Module 4: Model Optimization & Deployment: Model quantization, TensorRT acceleration, and edge device execution.

Who This Is For

Software developers, computer vision engineers, and robotics practitioners adding image processing capabilities to projects.