Object detection is a challenging task that involves locating and identifying objects of interest in images or videos. Faster R-CNN is a popular deep learning model that can achieve high accuracy and speed by combining region proposal networks (RPNs) and convolutional neural networks (CNNs). ResNet10 is a variant of ResNet, a family of CNNs that use residual connections to overcome the problem of vanishing gradients.
This project consists of an object detection web app using MERN stack, which consists of MongoDB, Express.js, React.js and Node.js. MongoDB is a NoSQL database that stores data in JSON-like documents. Express.js is a web framework for Node.js that handles routing and middleware. React.js is a front-end library for building user interfaces with components. Node.js is a runtime environment for executing JavaScript code outside the browser.
The steps are as follows:
- Train a Faster R-CNN model with ResNet10 backbone on your custom data using TensorFlow ³. You can use Roboflow to prepare and annotate your data easily.
- Save your trained model as a TensorFlow SavedModel format and convert it to TensorFlow.js format using tfjs-converter.
- Create an Express.js server that serves your model files and handles requests from the front-end. You can use multer to handle file uploads and tfjs-node to run inference on the server side.
- Create a React.js client that uses Redux for state management and axios for making HTTP requests. You can use react-webcam to capture images from the webcam and tfjs-react-native to run inference on the client side.