How to Train YOLOv8 Instance Segmentation on a Custom Dataset

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

Machine Learning model

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.

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

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

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.

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

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

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.

Use machine learning to identify players, refs and football field markings.

Object Detection model using Yolov8

From a selection of data from the Roboflow file https://universe.roboflow.com/landy-aw2jb/fracture-ov5p1/dataset/1, which represents a reduced but homogeneous version of that file, a model is obtained based on yolov10 with that custom dataset to indicate fractures in x-rays.

Realtime football analysis system made using YOLOv8

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

A football analysis system built using YOLOv5, Supervision, OpenCV in Python.

From dataset https://universe.roboflow.com/roboflow-100/bone-fracture-7fylg a model is obtained, based on yolov10, with that custom dataset, to indicate fractures in x-rays. The project uses 5 cascade models, if one does not detect fracture it is passed to another