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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.
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
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.
The Earthquake Emergency Response Robots project aims to create, develop, and implement systems specifically designed to handle post-earthquake situations. The main focus of the project is to build adaptable robots that come equipped with sensors and communication capabilities.
System designed to provide real-time assistance to visually impaired individuals by detecting obstacles in their path and helping them finding desire objects in their environment.