Deploying MobileNet with TensorFlow.js and Flask | Challenge

# Introduction This project guides you through the process of deploying a pre-trained MobileNetV2 model using TensorFlow.js within a Flask web application. MobileNetV2 is a lightweight deep neural network used primarily for image classification. TensorFlow.js enables running machine learning models directly in the browser, allowing for interactive web applications. Flask, a Python web framework, will serve as the backend to host our application. By the end of this project, you will have a running web application that classifies images on the fly using the MobileNetV2 model. ## 👀 Preview <video src="https://file.labex.io/namespace/718bace8-27a3-4200-a588-dde4041ceeb9/ml/project-deploying-mobilenet-with-tensorflowjs-and-flask/challenge-1/assets/predict.mp4" width="100%" autoplay loop muted></video> ## ðŸŽŊ Tasks In this project, you will learn to: - Export a pre-trained MobileNetV2 model from Keras to a TensorFlow.js compatible format. - Create a simple Flask application to serve your web content and model. - Design an HTML page to upload and display images for classification. - Use TensorFlow.js to load the exported model in the browser. - Preprocess images in the browser to match the input requirements of MobileNetV2. - Run the model in the browser to classify images and display the results. ## 🏆 Achievements By completing this project, you will learn: - How to convert a pre-trained Keras model into a format that can be used with TensorFlow.js, enabling ML models to run in the browser. - The basics of setting up a Flask application and how to serve HTML content and static files. - How to integrate TensorFlow.js into a web application to perform machine learning tasks client-side. - The process of preprocessing images in JavaScript to make them compatible with the input requirements of deep learning models. - How to make predictions using a deep learning model in the browser and display the results dynamically on the web page.

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Click the virtual machine below to start practicing