Develop an image classifier mobile application with Flutter, using TensorFlow Lite and Google’s Teachable Machine

In the previous article of this series on developing Flutter applications with TensorFlow Lite, we looked at how we can develop a Digit Recognizer using TensorFlow Lite.

In the second article of the series, we’ll keep working with TensorFlow Lite, this time focusing on implementing image classification to classify images between two classes. The application we are going to build will be able to classify whether an input image contains a horse or a human.

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Application and Use Cases

TensorFlow Lite gives us pre-trained and optimized models to identify hundreds of classes of objects, including people, activities, animals, plants, and places. Using Teachable Machine from Google, we can develop our own custom model using some of our own images. Teachable Machine 2.0 allows you to train machine learning models in the browser, without any ML code. You can train models on images, sounds, and poses, and then you can save the models you trained and use them on your own projects.

#heartbeat #tensorflow-lite #flutter #machine-learning #tensorflow