Computer Vision

From classifying a single image to tracking objects across video frames and generating entirely new images from noise, this teaching lab covers the full spectrum of modern computer vision. Across 8 progressive labs you will build, train, and deploy models in PyTorch on AMD GPUs, gaining hands-on experience with the architectures that power real-world vision systems.

Goals

• Build and train CNN and ResNet classifiers for image recognition
• Apply object detection (YOLOv9) and semantic segmentation (SegNet) to real-world datasets
• Use foundation models like Segment Anything (SAM) for zero-shot segmentation
• Implement multi-object tracking across video frames
• Explore generative models including VAE, Conditional VAE, and Diffusion Models

Classification and Detection (CV01 to CV03)

• CV01 — Image Classification with CNN

  Build a Convolutional Neural Network from scratch and train it on the CIFAR-100 dataset.

• CV02 — Deep Residual Networks (ResNet-50)

  Train a ResNet-50 classifier on CIFAR-100 and explore how residual connections solve vanishing gradients.

• CV03 — Object Detection with YOLOv9

  Apply YOLOv9 to locate and classify multiple objects in a single forward pass.

Segmentation and Tracking (CV04 to CV06)

• CV04 — Semantic Segmentation with SegNet

  Train a SegNet encoder-decoder on the CamVid dataset to assign a class label to every pixel.

• CV05 — Segment Anything (SAM)

  Run inference with Meta AI's Segment Anything Model for automatic and prompt-based segmentation.

• CV06 — Multi-Object Tracking with YOLOv8 + ByteTrack

  Track multiple objects across video frames with persistent identities using YOLOv8 and ByteTrack.

Generative Models (CV07 to CV08)

• CV07 — Variational Autoencoder (VAE and cVAE)

  Implement a VAE on MNIST and explore the Conditional VAE variant for controlled generation.

• CV08 — Diffusion Model

  Build and train a Diffusion Model that generates images by iteratively denoising random noise.

Explore the other teaching labs: Deep Learning, LLM from Scratch, Physics Simulation.