Module 11: Computer Vision
Master Convolutional Neural Networks (CNNs) for image processing.
What You'll Learn
- Introduction to Computer Vision and Visual Cortex
- Images, Pixels, and Color Models (RGB, Grayscale)
- Convolution Operations and Edge Detection
- Convolutional Neural Networks (CNNs)
- CNN Architectures (LeNet, AlexNet, VGGNet, ResNet)
- ImageNet and Large-Scale Recognition
- Transfer Learning and Fine-tuning
- Data Augmentation Techniques
- Object Detection (R-CNN to YOLO)
- Semantic and Instance Segmentation
- GANs for Image Generation
- Recent Breakthroughs (Vision Transformers, CLIP)
- Real-world Computer Vision Projects
CNN and RNN curriculum map (CNN track)
| Topic | Where to study |
|---|---|
| CNN foundation, architectures, math | Computer vision guide |
| Activations (Sigmoid, tanh, ReLU, Leaky ReLU, ELU, SELU) | Activation functions |
| CNN in PyTorch; augmentation; pretrained models | Frameworks PyTorch CV, Augmentation, Transfer learning |
| RNN / Transformer NLP | NLP module |
Topics Covered
1. Fundamentals
- Introduction to Computer Vision: Overview, applications, human visual system
- Images and Pixels: Digital images, RGB, Grayscale, color models
- Convolution Basics: Convolution operations, edge detection (Sobel, Canny, Prewitt)
- Spatial Arrangement: Padding, strides, spatial dimensions
2. Convolutional Neural Networks (CNNs)
- Convolutional Layers: Building blocks, feature detection
- Pooling Mechanisms: Max pooling, average pooling
- Working with RGB Images: Multi-channel convolutions
- Training Optimization: Batch normalization, dropout, callbacks
3. CNN Architectures
- LeNet (1998): Early CNN for digit recognition
- AlexNet (2012): Breakthrough in ImageNet, key innovations
- VGGNet (2014): Deep networks with small filters
- ResNet (2015): Residual connections, skip connections
- ImageNet: Large-scale recognition challenges and impact
4. Transfer Learning
- Pre-trained Models: Leveraging ImageNet-trained models
- Fine-tuning: Adapting to specific tasks
- Feature Extraction: Using CNNs as feature extractors
- When to use: Limited data, faster training, better performance
5. Data Augmentation
- Why Augment: Increasing dataset size and diversity
- Techniques: Rotation, flipping, scaling, color jitter, cropping
- Implementation: Keras ImageDataGenerator, Albumentations
- Best Practices: Realistic augmentations, avoiding over-augmentation
6. Object Detection
- Evolution: From R-CNN to YOLO
- R-CNN Family: R-CNN, Fast R-CNN, Faster R-CNN
- YOLO: You Only Look Once for real-time detection
- Applications: Face detection, autonomous vehicles, surveillance
7. Segmentation
- Semantic Segmentation: Pixel-level classification
- Instance Segmentation: Distinguishing individual objects
- Mask R-CNN: Advanced segmentation architecture
- Applications: Medical imaging, autonomous vehicles
8. Advanced Topics
- GANs: Generative Adversarial Networks for image synthesis
- Diffusion Models: Understanding diffusion process for image generation
- Stable Diffusion: Latent diffusion models with Hugging Face integration
- VAEs: Variational Autoencoders for image generation
- Vision Transformers: Transformer architecture for vision
- Recent Breakthroughs: CLIP, DALL-E, Stable Diffusion
- Emerging Technologies: Future directions in computer vision
Learning Objectives
By the end of this module, you should be able to:
- Understand computer vision fundamentals (images, pixels, convolution)
- Build CNN architectures from scratch (LeNet, AlexNet, VGGNet, ResNet)
- Implement edge detection and image processing techniques
- Apply transfer learning effectively
- Augment image data for better model performance
- Understand and implement object detection (YOLO, R-CNN)
- Work with semantic and instance segmentation
- Understand GANs and recent vision breakthroughs
Projects
- MNIST with CNN: Improve digit recognition
- CIFAR-10 Classification: Classify natural images
- Cat vs Dog Classifier: Binary image classification
- Transfer Learning Project: Use pre-trained models
- Custom Image Classifier: Your own dataset
Key Concepts
- Convolution: Detect local patterns
- Pooling: Reduce spatial dimensions
- Receptive Field: Area of input each neuron sees
- Feature Maps: Outputs of convolution layers
- Transfer Learning: Leverage pre-trained knowledge
Documentation & Learning Resources
Official Documentation:
Free Courses:
- CS231n - Stanford: Best CV course, completely free
- CS231n YouTube Lectures: Full course videos
- Computer Vision (Coursera): Free audit available
- Fast.ai Computer Vision: Free practical course
Tutorials:
- CNN Tutorial (TensorFlow)
- Transfer Learning Tutorial
- Image Classification Guide
- Object Detection Tutorial
Video Tutorials:
Practice:
Additional Resources:
- Advanced Topics →: Advanced architectures, object detection, optimization
- Project Tutorial →: Step-by-step CIFAR-10 classification project
- Quick Reference →: Quick lookup guide for computer vision
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