NVR with realtime local object detection for IP cameras
Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.
Free to use online tool for labelling photos. https://makesense.ai
File explorer made for pictures
📖 👆🏻 Links Detector makes printed links clickable via your smartphone camera. No need to type a link in, just scan and click on it.
Creepy stalking tool to process security camera motion triggered images and sort seen objects in different categories, detect license plates and faces. Has PWA ready web front end. Meant to make property monitoring faster without need to watch video recordings.
Tiny YOLO v2 object detection with tensorflow.js.
PiGallery: AI-powered Self-hosted Secure Multi-user Image Gallery and Detailed Image analysis using Machine Learning, EXIF Parsing and Geo Tagging
Web-based real-time object detection for YOLO model.
A react component that helps labeling images for object detection
📺 Instill Console for 🔮 Instill Core: https://github.com/instill-ai/instill-core
Use custom trained Cloud Annotations models with TensorFlow.js
Experience the power of Clarifai’s AI platform with the nodejs SDK. 🌟 Star to support our work!
Real-time Webcam Object / Face Detection with MediaPipe
A collection of React hooks that enable developers to integrate artificial intelligence functionalities using machine learning models directly into their web applications.
📱An React Native app powered by the Viro library that detects and displays images and objects in real-time, presenting lifelike 3D models through augmented reality (AR)🕶️
An AI-powered web application leveraging Next.js 14 and TensorFlow.js for real-time object detection. Utilizing Tensorflow model for accurate detection, webcam integration for live video streams, and Media Recorder API for seamless recording.
A simple wrapper to use keras-retinanet models with tensorflowjs.
Snapfolia is a leaf classifier web application using YOLOv8, a deep-learning model.
🌱🔎 This is my graduation project. The objective is to detect the disease in the coffee leaf and the contamination percentage.
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