Computer Vision

Description

This Computer Vision training course teaches you how to train machines to see and understand images and videos for real-world applications like facial recognition, object detection, autonomous vehicles, medical imaging, and quality inspection. You will learn the foundations of image processing, deep learning architectures like CNNs and Vision Transformers, and how to deploy production computer vision models using OpenCV, PyTorch, TensorFlow, and YOLO.

Course Content

Module 1: Foundations of Computer Vision

How computers see images
Pixels, color spaces, and features
Traditional vs. deep learning approaches

Module 2: Image Processing with OpenCV

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Filters, edges, and contours
Thresholding and morphological operations
Real-time video processing

Module 3: Convolutional Neural Networks

Understanding CNNs and convolutions
Popular architectures: ResNet, VGG, EfficientNet
Training CNNs from scratch
Transfer learning and fine-tuning

Module 4: Object Detection

YOLO: real-time object detection
Faster R-CNN and single-shot detectors
Training custom detectors
Benchmarking and accuracy tradeoffs

Module 5: Segmentation and Recognition

Semantic and instance segmentation
U-Net and Mask R-CNN
Facial recognition and verification
Pose estimation and action detection

Module 6: Vision Transformers

Transformers applied to vision
ViT and Swin Transformers
CLIP and multimodal vision
When ViTs beat CNNs

Module 7: Industry Applications

Autonomous vehicles and robotics
Medical imaging and diagnostics
Retail analytics and loss prevention
Agriculture, sports, and security

Module 8: Deployment and Capstone

Optimizing models for speed
Edge deployment: Raspberry Pi, mobile
Cloud deployment and APIs
Build your own computer vision project

Duration: 8 – 10 weeks

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