Traffic Light Classification with Node.js, React.js, TensorFlow, and COCO-SSD | AI ML Tutorial скачать в хорошем качестве

Traffic Light Classification with Node.js, React.js, TensorFlow, and COCO-SSD | AI ML Tutorial

In this tutorial, we'll walk you through creating a traffic light classification app using Node.js and React.js, powered by TensorFlow and the COCO-SSD model. We'll cover everything from setting up the development environment to building both the backend and frontend, and finally connecting them together. You'll learn how to: Set up a Node.js server to handle image processing and predictions Use React.js to build a user-friendly interface for uploading traffic light images Integrate TensorFlow with COCO-SSD for traffic light detection and classification Display the predicted traffic light color in the React frontend Whether you're a beginner or an experienced developer, this tutorial will help you understand how to leverage AI and machine learning for real-world applications. Documentation: Prerequisites: Install Python: TensorFlow.js requires Python for building native modules. Ensure you have Python installed on your system and that it's added to your PATH. Download Python from python.org. During installation, make sure to check "Add Python to PATH". Install Visual Studio Build Tools: Download Visual Studio Build Tools from Microsoft's website: Visual Studio Build Tools. During installation, select "Desktop development with C++" and install. Install Node Version Manager (nvm) for Windows: Download nvm-setup.zip from the nvm releases page on GitHub. Install nvm and use it to manage Node.js versions. nvm install 16 nvm use 16 Project Setup: Backend Setup: mkdir traffic-light-classification cd traffic-light-classification npm init -y npm install express multer cors @tensorflow/tfjs-node @tensorflow-models/coco-ssd sharp@0.28.3 jpeg-js Create an uploads directory: mkdir uploads Frontend Setup: npx create-react-app client cd client npm install axios npm install react-spinners If you found this tutorial helpful, please like, share, and subscribe for more AI and machine learning tutorials!