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Learn to create a fall detection app using YOLOv7 pose estimation, covering UI design, push-up counting logic, and implementation on both CPU and GPU. Explore real-world applications of computer vision in healthcare.
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.
Comprehensive Python-based OpenCV tutorial covering fundamentals to advanced applications, including face detection, object tracking, and image classification. Ideal for aspiring computer vision professionals.
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.
Build a Raspberry Pi pan-tilt tracking camera using OpenCV AI Kit. Learn hardware setup, coding, and dataset creation with Roboflow for real-time object detection and camera movement at 30 FPS.
Step-by-step guide to installing Detectron2 for computer vision projects using OpenCV and Python, covering essential requirements, software setup, and testing procedures.
Comprehensive introduction to machine learning theory, covering algorithms, neural networks, and computer vision topics through engaging whiteboard animations designed for beginners.
Build a real-time mask detection access control system using Raspberry Pi, OpenCV AI Kit, and a stepper motor. Learn hardware setup, coding, and dataset annotation with Roboflow for this practical AI application.
Learn to detect chess pieces using YOLOv5 and OpenCV in Python. Explore object detection techniques, train a custom model, and implement computer vision for chess analysis.
Build a real-time social distancing detection system using Raspberry Pi and OpenCV AI Kit. Learn to implement computer vision for monitoring and alerting, with depth capability and high performance on an embedded platform.
Build a real-time AI surveillance camera using Raspberry Pi and OpenCV AI Kit. Detect objects, people, and packages with MobileNet SSD at 30 FPS. Includes hardware setup, coding, and Roboflow integration for custom object detection.
Master the process of training machine learning models for soil fertility analysis using XGBoost, from dataset preparation to API integration, achieving 88% accuracy in predicting soil health levels.
Master backend development for soil analysis by integrating Flask APIs, XGBoost models, and GPT-powered insights. Create robust endpoints for IoT sensor data processing and AI-driven agricultural recommendations.
Master backend development for an AI-powered rice crop analysis system using YOLOv8, LangChain, and Flask to detect nutrient deficiencies and provide chatbot-based explanations.
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