Lilly Tech Systems
Learn Computer Vision
Teach machines to see and understand visual information. From image processing fundamentals to state-of-the-art detection, segmentation, and generative models — all for free.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is Computer Vision? How computers see, applications, and the evolution of the field.
2. Image Processing
Image representation, color spaces, filtering, edge detection, and OpenCV fundamentals.
3. Object Detection
R-CNN family, YOLO, SSD, anchor boxes, non-max suppression, and transfer learning.
4. Image Classification
CNN architectures from LeNet to Vision Transformers, transfer learning, and data augmentation.
5. Segmentation
Semantic, instance, and panoptic segmentation. U-Net, Mask R-CNN, DeepLab, and SAM.
6. Advanced Topics
GANs, diffusion models, video understanding, 3D vision, pose estimation, OCR, and CLIP.
7. Best Practices
Dataset creation, augmentation, model selection, training tips, evaluation, and deployment.
What You'll Learn
By the end of this course, you will be able to:
Process Images
Manipulate images using OpenCV: resize, filter, detect edges, and transform color spaces.
Detect and Classify
Build object detection and image classification systems using modern deep learning models.
Segment Images
Perform pixel-level segmentation using U-Net, Mask R-CNN, and the Segment Anything Model.
Generate Images
Understand generative models like GANs and diffusion models that create images from text.