notebooks

This repository provides a comprehensive collection of Jupyter notebooks for learning computer vision. It covers foundational architectures like ResNet to cutting-edge models including RF-DETR, YOLO11, SAM 3, and Qwen3-VL. The tutorials span tasks such as object detection, instance segmentation, pose estimation, keypoint detection, object tracking, OCR, and JSON data extraction. Many notebooks include fine-tuning guides for custom datasets, while others demonstrate zero-shot inference capabilities. Each notebook can be opened directly in Google Colab, Kaggle, or SageMaker Studio Lab, and many include complementary materials such as YouTube videos, blog posts, and links to original papers. The collection is actively maintained and grows over time, making it a valuable resource for practitioners and researchers in computer vision.