
Master image annotation with a Python-based tool. Learn installation, project creation, manual and semi-automatic annotation techniques, class organization, and exporting to various formats. Explore additional features for data management and augmentatio…

Explore the Darknet/YOLO framework for Computer Vision, its history, and practical applications through examples. Learn how to detect objects in images and videos.

Explore autonomous racecar development: perception, localization, mapping, path planning, and vehicle control. Gain insights into creating self-driving vehicles using advanced technologies.

Explore efficient AI/LLM inference on edge cloud using WebAssembly. Learn to create lightweight, portable applications for media processing, vision, and language models, running at native speed across CPUs and GPUs.

Comprehensive tutorial on implementing YOLOv3 object detection algorithm from scratch using PyTorch, covering architecture, dataset loading, loss function, and training setup.

Learn object detection fundamentals and apply skills in four practical projects, including car counting, people counting, PPE detection, and poker hand recognition using YOLO and computer vision techniques.

Step-by-step guide to train and deploy YOLOv7 object detection on custom datasets using Google Colab's free GPU. Covers model evaluation and inference on images, videos, and webcam.

Learn to build a real-time weed detection model using YOLOR and OpenCV in Python. Extract weed coordinates and implement computer vision techniques for agricultural applications.

Learn to create a Bernie Sanders detector using YOLOv4/v5, OpenCV, and Roboflow. Build datasets, train models, and implement real-time detection on various platforms.

Aprende a implementar YOLO 11 desde cero, desarrollando aplicaciones prácticas y realizando ajustes de fine-tuning para optimizar el rendimiento de detección de objetos.

Learn to build an AI-powered object detection system using Python, OpenCV, and YOLO, covering essential tools, dataset preparation, and model training techniques.

Master real-time vehicle detection and tracking using YOLO11, implementing class-wise counting with BoT-SORT and ByteTrack algorithms for traffic analysis and smart city applications.