A computer vision-based project that uses YOLOv8 to detect and classify plant leaves in real time, helping identify healthy and diseased plants efficiently to support smart agriculture solutions.

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.

Soccer analysis using YOLOv8 & Supervision ByteTrack

How to Train YOLOv8 Instance Segmentation on a Custom Dataset

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.

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.

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.

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

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

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.

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

🌿 Detect and classify plant leaves in real-time using YOLOv8, enhancing crop health and productivity through early disease identification.

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

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.

ObjectDetect is an Android App that lets users select a gallery image and run cloud-based object detection using a Roboflow workflow. It displays both the original and processed images with bounding boxes, supports full-screen viewing with zoom and pan, and features a modern UI built with Jetpack Compose, Ktor, and Koin in a clean architecture.

Deep learning project is designed to detect small-sized rooftop photovoltaic (PV) panels using satellite imagery

Machine Learning model

AI-Powered UI Element Detection in Website Screenshots using YOLOv8

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

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