Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER

Real-time object detection on Android using the YOLO network with TensorFlow

Android YOLO real time object detection sample application with Tensorflow mobile.

Object detection using deep learning with Yolo, OpenCV and Python via Real Time Streaming Protocol (RTSP)

Flutter real-time object detection App with Paddle-Lite and YOLO v3.

Yolo (Real time object detection) model training tutorial with deep learning neural networks

Webcam video stream with real-time YOLO object detection, built with Ort, Tonic and Axum.

Real-time YOLO Object Detection in Phoenix with Python

YOLO Real Time Object Detection (YOLO) with OpenCV and Python.

🚗🚦 Introducing our Smart Adaptive Traffic Management System! 📷🧠 Using YOLO v8 object detection & CCTV cameras, we analyze real-time traffic. By tracking vehicles 🚙 & pedestrians 🚶, we enhance signal timings for better flow, safety, & reduced congestion. Drive smarter with us! 🌐🏞️

A solution code for the real time object detection with WebRTC and YOLO article - https://softwarescalability.com/editorial/real-time-object-detection-with-webrtc-and-yolo

An IoT-based multi-person fall detection system using YOLO and ESP32-CAM. This project integrates advanced object detection with ESP32-CAM for real-time fall detection in multi-person environments, suitable for smart surveillance, healthcare, and security systems

Complex YOLO ROS is a 3D object detection system interfaced with ROS, enabling real-time robotics applications. It utilizes Lidar data and deep learning techniques for accurate detection and localization of objects in complex environments

YOLO integration with ROS for real-time object detection

Real-time camera feed object detection with React.js, WASM, Rust, Candle ML, and YOLO

The self-checkout portal at the supermarket gives the idea to the classification of fresh fruits and vegetables. Nowadays, more self-checkout portals are added to the time saving of the customers at the supermarket. When it comes to fresh fruits and vegetables, It still needs to enter manually into the computer for purchase and, it is a bit time-consuming and increases cheating (By putting the wrong item name). We can make use of the camera at self-checkout to get a prediction of the items using machine learning. For example, when we put tomatoes on the counter, it detects the tomatoes through a semi-transparent bag and gives various tomatoes as a list. The problem with different object detection models is to see through semi-transparent bags to classify the image. You Only Look Once (YOLO) object detection did this job well If we train the model correctly. The main stages of Object detection are data acquisition, Augmentation, Model training, Model Evaluation, and Deployment. It gives 99.4% max accuracy on the training and testing dataset to classify 14 different classes for fruits and vegetables. On real-life images, it provides approx 90% accuracy on images to classify. Prediction execution performs under a sec is considered a good result for the self-checkout terminal.

Real time object detection demo App with Yolo on iOS based on tensorflow framework

The application is developed using Python3, OpenCV and with concepts of Neural Networks it is trained on DarkNet 53 and for the real time object detection I am using Yolo v3. It detects the 1 hand representation of number 1-10 which will be pointed by the user in real time using American Sign Language

YOLO iOS SDK is a SPM package that allows easy integration of the YOLO object detection model on iOS platforms. Optimized for real-time object detection, this SDK works with CoreML support.

This repository contains the code for real-time object detection.I have used darkflow and YOLO pre-trained model.

Multi-stream video inference with Ultralytics YOLO - Display multiple video streams in a grid layout with real-time object detection.

🚀 Advanced AI-powered surveillance system for Andhra Pradesh Government Hackathon. Real-time camera monitoring with YOLO object detection, intelligent analytics, smart alerts, and comprehensive dashboard. Built with Next.js, TypeScript, Python backend, and modern web technologies.

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🧠 Real-time object detection in mixed reality on Meta Quest 3 using Unity, Sentis (YOLO), and passthrough camera. Anchors 3D bounding boxes to real-world objects with Scene Understanding and live detection overlay UI.

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Real-time object detection on Raspberry Pi 5 with the AI Camera (Sony IMX500): export YOLO to IMX, package to .rpk, and run the Picamera2 demo.

The car parking space detection project using YOLO is a computer vision system designed to detect the availability of parking spaces in a parking lot in real-time. The system is based on the state-of-the-art object detection algorithm YOLO and requires a dataset of parking lot images with labeled parking spaces.

A web application using Flask for backend and HTML/CSS for frontend, implementing real-time object detection and classification in images, videos, and live streams with the YOLOv5 algorithm.

Real-time 3D room reconstruction on Meta Quest 3. GPU TSDF + Surface Nets meshing, passthrough texturing, on-device texture refinement with Sobel normals, AI object detection (YOLO/Sentis + GPU NMS), MRUK scene understanding, Gaussian Splat training & rendering, multi-scan persistence with spatial anchors. Unity 6 URP package.

This AIM of this repository is to create real time / video application using Deep Learning based Object Detection using YOLOv3 with OpenCV YOLO trained on the COCO datasets. The COCO dataset consists of 80 labels.