Computer Vision with CNNs, OpenCV & Transfer Learning

Computer Vision (CV) has become a cornerstone of modern Artificial Intelligence, enabling machines to see, understand, and interpret visual data. From autonomous vehicles and healthcare imaging to retail analytics and industrial automation, computer vision is transforming industries. This 7-day intensive corporate training program focuses on mastering computer vision techniques using Convolutional Neural Networks (CNNs), OpenCV, and Transfer Learning with frameworks such as TensorFlow and PyTorch. Participants will gain the skills to build and deploy vision-based AI solutions for real-world enterprise applications, learning through a mix of theory, implementation, and business case studies.

Objectives of the Training

Understand the core concepts of computer vision and its industrial applications.
Learn to implement Convolutional Neural Networks (CNNs) for image classification and object detection.
Master OpenCV for image processing, feature extraction, and video analysis.
Explore transfer learning and fine-tuning pre-trained models (ResNet, VGG, EfficientNet).
Apply deep learning for enterprise use cases such as facial recognition, defect detection, and visual analytics.
Integrate computer vision pipelines with cloud and edge platforms for scalable deployment.

Prerequisites

Working knowledge of Python programming.
Basic understanding of linear algebra, calculus, and probability.
Familiarity with machine learning concepts and frameworks (TensorFlow, PyTorch).
Exposure to image data or interest in vision-based AI applications.

What You Will Learn

Fundamentals of digital image processing and OpenCV operations.
Implementation of CNNs and understanding convolutional operations.
Object detection and image segmentation techniques.
Transfer learning using pre-trained models for enterprise-grade tasks.
Best practices for training, evaluating, and deploying vision-based models.
Integration of computer vision solutions into cloud and edge environments.

Target Audience

This training is designed for Data Scientists, Computer Vision Engineers, AI Practitioners, and Software Developers who want to master end-to-end computer vision model development and deployment. It is also suitable for Technical Managers and Innovation Leads aiming to understand how vision AI can enhance operational efficiency and product innovation in enterprise settings.

Detailed 7-Day Curriculum

Day 1 – Introduction to Computer Vision & Image Processing (6 Hours)

Session 1: Fundamentals of Computer Vision – Evolution and Business Applications.
Session 2: Basics of Digital Images – Pixels, Channels, and Color Spaces.
Session 3: Introduction to OpenCV – Reading, Transforming, and Manipulating Images.
Hands-on: Image Filtering, Edge Detection, and Histogram Equalization.

Day 2 – Image Features and Object Detection (6 Hours)

Session 1: Feature Detection and Extraction – SIFT, SURF, and ORB Algorithms.
Session 2: Template Matching and Contour Detection Techniques.
Session 3: Object Tracking in Videos with OpenCV.
Hands-on: Real-Time Object Detection and Motion Tracking.

Day 3 – Convolutional Neural Networks (CNNs) Fundamentals (6 Hours)

Session 1: Understanding Convolutions, Filters, and Feature Maps.
Session 2: Designing and Training CNNs using TensorFlow and PyTorch.
Session 3: Pooling, Activation, and Normalization Layers Explained.
Case Study: Image Classification for Manufacturing Defect Detection.

Day 4 – Advanced CNN Architectures and Transfer Learning (6 Hours)

Session 1: Deep Architectures – ResNet, Inception, and VGG Networks.
Session 2: Transfer Learning and Fine-Tuning for Domain-Specific Tasks.
Session 3: Data Augmentation and Regularization Techniques.
Hands-on: Fine-Tuning a Pre-Trained CNN for Retail Product Recognition.

Day 5 – Object Detection and Image Segmentation (6 Hours)

Session 1: Object Detection Models – R-CNN, Fast R-CNN, and YOLO Architectures.
Session 2: Image Segmentation with U-Net and Mask R-CNN.
Session 3: Evaluating Model Performance with Precision, Recall, and mAP.
Case Study: Automated Defect Detection for Industrial Applications.

Day 6 – Vision Model Optimization and Deployment (6 Hours)

Session 1: Model Optimization with Pruning and Quantization.
Session 2: Deployment on Cloud (AWS, Azure) and Edge Devices (NVIDIA Jetson).
Session 3: Integrating Vision Models into Enterprise Pipelines via REST APIs.
Workshop: Building an End-to-End Vision AI System for Smart Surveillance.

Day 7 – Capstone Project & Future of Vision AI (6 Hours)

Session 1: Capstone Project Development – Design and Implementation.
Session 2: Group Presentations and Peer Review.
Session 3: The Future of Vision AI – Multimodal Perception and 3D Vision.
Panel Discussion: Ethical AI and Bias in Computer Vision.

Capstone Project

The capstone project challenges participants to build a complete computer vision pipeline tailored to an enterprise application. Sample projects include automated visual inspection, facial recognition-based authentication, or real-time traffic monitoring. Participants will design, train, and deploy their models using CNNs and transfer learning, supported by cloud or edge deployment frameworks.

Future Trends in Computer Vision

The future of computer vision lies in multimodal AI, real-time inference, and autonomous perception systems. Emerging trends include vision transformers (ViT), self-supervised learning, and AI-powered 3D reconstruction. Enterprises are increasingly leveraging vision AI for predictive maintenance, remote monitoring, and digital twins, making computer vision one of the most valuable capabilities in the AI-driven economy.