How to Train YOLOv8 Instance Segmentation on a Custom Dataset

Traffic Sign Detection and Warning System in real time with Custom Dataset & YOLOV8.

An object detection task completed with YOLO11n (nano) network for dental application.

This project implements a YOLOv8 model to detect and classify various skin diseases from images. The model is trained on a dataset of labeled images and can identify different types of skin conditions in real-time.

Facial Expression Recognition System using YOLOv9 & Flask. Detects 5 emotions (Angry, Happy, Natural, Sad, Surprised) from images/live camera with mAP50 of 0.731. Features a web interface with file uploads, real-time processing, & emoji feedback. Built with Python, OpenCV, Flask, HTML/CSS/JS. Ideal for HCI & emotion analysis.

This repository demonstrates how to fine-tune YOLOv11n on multiple fire detection datasets. It provides a complete pipeline for combining multiple datasets from Roboflow, training a unified model, and evaluating its performance.

Machine Learning model

Utilize YoloV8 for object detection of copper ore in Albion Online game with farming capabilities.

AI-Powered UI Element Detection in Website Screenshots using YOLOv8

The Left_Udjat project focuses on detecting jet aircraft objects in images and videos using the advanced YOLOv11 model. The goal is to build a robust and efficient detection system capable of identifying specific fighter jet types such as the F22, F35, and J20.

Detect football players in videos using YOLOv5 for training and YOLOv8 for inference. The dataset is sourced from Roboflow and includes 663 annotated images. The project involves pre-processing, augmentation, and model training for accurate player detection.

The road sign recognition system of the Russian Federation, which uses an already prepared model for object detection and image segmentation in real time to improve road safety

Proyek ini mengembangkan sistem cerdas untuk mendeteksi kepadatan lalu lintas serta pengendara motor yang tidak menggunakan helm, dengan kemampuan analisis secara real-time maupun dari rekaman video.​

From dataset https://universe.roboflow.com/drone-detection-pexej/drone-detection-data-set-yolov7/dataset/1 a model is obtained, based on yolov10 to detect drones in images. Predictions from several models are used in cascade to obtain the optimal result.

Fire detection using YOLOv8 involves utilizing a state-of-the-art object detection model to accurately identify fire in images or video feeds in real-time, leveraging its advanced capabilities to enhance early warning systems.

a real-time device for sidewalk danger detection and warnings

CopyMe is a project aimed at optimizing sports performance, using computer vision

Custom Yolov8x-cls edge model deployment and training to classify trash vs recycling.

This project focuses on leveraging the YOLO-NAS model for Smoke Detection.

This project demonstrates how to track a ball in a video showcasing a Tennis game by training a custom YOLO detection model. The model is trained not only for ball detection but also interpolation to handle areas where the tracking fails.