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

Automate the YOLO training by Ultralytics for custome COCO datasets

This project uses YOLO models for efficient object detection with a Streamlit interface. Users can upload images or video streams for real-time detection. It supports YOLOv8, YOLOv9, and YOLOv10; offering flexibility and high accuracy in various scenarios.

A web application that use a YOLOv10m model, got mAP50 of 0.934, trained on the VietFood57 dataset to perform real-time Vietnamese food recognizing.

AutoGate AI is an advanced vehicle access system that uses YOLOv10 and EasyOCR for real-time license plate detection and automated gate control.

From dataset https://www.kaggle.com/datasets/davidbroberts/brain-tumor-object-detection-datasets/data?select=sagittal_t1wce_2_class a model is obtained, based on yolov10 to indicate a brain tumor type: sagittal_t1wce

Object Detection with YOLOv10

Adjusted Ultralytics YOLO11 🚀. Current problem is the lack of attention modules which could be used in cgf configs.

This project implements an automated brain tumor detection system using the YOLOv10 deep learning model. It utilizes a robust MRI dataset for training, enabling accurate tumor identification and annotation. An interactive Gradio interface allows users to upload images for real-time predictions, enhancing diagnostic efficiency in medical imaging.

Learn how to use YOLO for object detection and how to train your own models.

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.

Optimization of YOLOv8 and YOLOv10 inference with TensorRT

This is part of a project from IE University. It contains a notebook that walks you through creating a model for predicting rats on images using Computer Vision. It has two steps: Automatic annotation with Grounding DINO and Building the model with YOLOv10. We will leverage on Ultralytics and Roboflow platform

Damaged building detection model with YOLOv10 on Rescuenet dataset

This is an introductory walkthrough on how to use YOLOv10.

A FIRE DETECTION GUI APPLICATION

Advanced Deep Learning framework for predicting the future position of aircraft refuelling ports using YOLOv10 and a custom SizPos-GRU sequence model to improve automated refuelling systems. Thesis Paper 👇🏻

From dataset https://universe.roboflow.com/test-svk7h/brain-tumors-detection/dataset/2 a model is obtained, based on yolov10 to indicate tumors in images of brains.

From dataset https://www.kaggle.com/datasets/safurahajiheidari/kidney-stone-images/data (from Roboflow) a model is obtained, based on yolov10, with that custom dataset, to indicate Kidney_Stone in x-rays.

Optical Music Recognition using Deep Learning