Deep learning model constructed using the same idea of yolo but for Risiko! object detection

Tensorrt codebase to inference in c++ for all major neural arch using onnx

YOLOv9 Face 🚀 in PyTorch > ONNX > CoreML > TFLite

This project implements a traffic sign detection system using the YOLOv9 object detection model.

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 project focuses on real-time analysis of surveillance camera-generated video data, introducing an automated detection approach that leverages smart networks and algorithms.

This project is built to recognize text on license plates in pictures using YOLOv9 and EasyOCR

Traffic Signal Controll by Tracking, counting and speed estimation of vehicles on surveillance cameras using YOLO v9 and Reinforcement Learning

This repository utilizes the Triton Inference Server Client, which streamlines the complexity of model deployment.

This project utilizes the YOLO (You Only Look Once) object detection algorithm combined with the ByteTrack multi-object tracking algorithm to monitor and count people passing a specified marker. The direction of movement (right-to-left or left-to-right) is recorded, and counters are incremented accordingly.

Repository containing implemetation and documentation of master's thesis Object detection and segmentation in historical encrypted manuscripts at at Faculty of Electrical Engineering and Information Technology of Slovak University of Technology in Bratislava (FEI STU).

This is a Robot which can help you on our daily life with the Humanoid features it can be multitasking (help the unabled to reach objects, assistance on a daily basis)

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.

Real time fruit detection with YOLOv9

This project extends the YOLOv9 object detection model to detect additional custom classes specific to autonomous driving, including various traffic signs and cones, alongside the existing 80 COCO dataset classes. By curating and annotating new datasets, we aim to retain high detection accuracy across both original and new classes.

This is the tensorrt inference code for yolov9 instance segmentation.

YOLOv9 (Ultralytics) Python interface for training, validating and running detection on waste detection dataset + detection using SAHI.

Docker Production Ready YoloV9: REST Service for x-ray fracture detection

This a simple comparison and benefits of the pre-trained weights model vs the random weights model

Prototype of an intelligent safety system for detecting driver drowsiness