a GUI application, which uses YOLOs (YOLOv8, YOLO11, YOLOv13) for Object Detection/Tracking, Human Pose Estimation/Tracking from images, videos or camera

A sample android application of live object detection for any YOLOv8 detection model

Road damage detection application, using YOLOv8 deep learning model trained on Crowdsensing-based Road Damage Detection Challenge 2022 dataset

An application suite including an open-source inference server and web UI to deploy any YOLOv8 model to NVIDIA Jetson devices and visualize captured streams, with one line of code.

Welcome to the Fish-Detection-YOLOv8 repository! This project focuses on detecting fish species using the YOLOv8 object detection algorithm. It aims to provide accurate and efficient fish detection models for applications such as marine biology research and automated fishing systems.

This is a GUI application that integrates YOLOv8 object recognition with OpenAI's GPT-3 language generation model.

The Live Object Detection web application is a Flask-based application that allows users to perform real-time object detection on a live video stream or a video URL. It utilizes the YOLOv8 (You Only Look Once) model for object detection and provides an interactive interface to control various settings for the video stream and detection.

This repository serves object detection using YOLOv8 and FastAPI. With YOLOv8, you get a popular real-time object detection model and with FastAPI, you get a modern, fast (high-performance) web framework for building APIs. The project also includes Docker, a platform for easily building, shipping, and running distributed applications.

How to integrate the YOLOv8 ONNX model into your Next.js 13 application.

YOLOv8 + PyTorch + CV2 Python-Flask Web application for Object Detection and Classification

Utilizing YOLOv8, my GitHub project implements personalized data for training a custom facial recognition system, improving accuracy in identifying diverse facial features across real-world applications.

When-to-bull AI is an advanced application designed for detecting patterns in stock and cryptocurrency charts using deep learning techniques. This project extends their approach by implementing the model with YOLOv8 and integrating it into a user-friendly Streamlit app.

This repository demonstrates YOLOv8-based license plate recognition with GCP Vision AI integration, enabling versatile real-world applications like vehicle identification, traffic monitoring, and geospatial analysis while capturing vital media metadata for enhanced insights.

YOLOv8 deep learning model, integrated into a Django + Django REST Framework application.

This project implements a Wrinkle Detection application using YOLOv8 for segmentations. The application is built with Streamlit and allows users to upload images for wrinkle detection of human faces. Segmentation using YOLOv8s (small) finetuned model.

This project provides a comprehensive solution for performing human action detection using YOLOv8, a powerful object detection model, integrated with the Roboflow platform for efficient dataset management. Human action detection is a vital task in computer vision, with applications ranging from video surveillance to human-computer interaction.

This powerful and flexible application, developed using PyQt6, processes 4 video streams in real-time with the YOLOv8-OBB model, supporting cameras, video files, for efficient object detection and result saving, tailored to meet specific client requirements.

Utilizing YOLOv8, my GitHub project implements personalized data for training a custom personal recognition system, improving accuracy in identifying diverse personal features across real-world applications.

An AI-powered, voice-controlled 6-DOF robotic arm built on Raspberry Pi 5. It features YOLOv8 object detection, smooth servo motion, real-time camera preview, custom motion presets, and hands-free operation using offline speech recognition. Designed for precision manipulation and interactive robotics applications with a 3D printable frame.

A real-time Stereo Depth Estimation and Object Detection system using YOLOv8. Designed for beginners, this project provides a practical starting point for exploring and extending computer vision applications such as robotics, autonomous vehicles, and security systems. The code is a simplified and beginner-friendly version that is easy to understand

🚗 Car Counter: A real-time application for vehicle counting and classification using advanced algorithms like YOLOv8 and ByteTrack. Features a user-friendly dashboard with Streamlit. Collaboratively developed by Richard Zimmermann and Moritz von Angern.

Next.js application with models loaded on client. Only TFJS SSD model, will include custom YOLOv8 model soon.

The Uniform Detection Software is an advanced application that utilizes the YOLOv8 algorithm for real-time object detection to determine whether University of Southern Mindanao (USM) students are wearing complete or incomplete uniforms.

This project serves as a foundational template for integrating PaddleOCR and YOLOv8 into Android applications, enabling powerful text recognition and object detection capabilities.

Crop Disease Detection System: A machine learning-based web application that allows users to upload images of plants, detects potential diseases using YOLOv8, and provides detailed disease information, prevention tips, and product recommendations from Amazon India. Built with Streamlit for an interactive experience.

YOLOv8 Object Detection project showcasing real-time object detection with Ultralytics YOLOv8. Includes training, testing, and deployment scripts, making it easy to adapt the model for custom datasets and real-world applications.

A Python application built using YOLOv8 that can detect farm animals.

Web Application using YOLOv8 algorithm for fire ant and nests detection in pair with Streamlit framework for Real-Time Object Detection and tracking in video streams. Detects rifa nest and ants easily!

Snapfolia is a leaf classifier web application using YOLOv8, a deep-learning model.

ONNX Runtime Mobile object detection using yolov8 iOS sample application