This project is a sample of how to perform Image Classification using TensorFlow Lite in Flutter. It includes support for both still images and live camera streams.
| Android | iOS | Linux | Mac | Windows | Web | |
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| file | ✅ | ✅ | ✅ | ✅ | ✅ | |
| life | ✅ | ✅ | 🚧 | 🚧 | 🚧 |
To build the project, you must first download the MobileNet TensorFlow Lite
model and its corresponding labels. You can do this by
running sh ./scripts/download_model.sh from the root folder of the repository.
- You can use Flutter-supported IDEs such as Android Studio or Visual Studio. This project has been tested on Android Studio Flamingo.
- Before building, ensure that you have downloaded the model and the labels by following a set of instructions.
- All heavy operations are performed in a separate background isolate.
- This sample supports for still images and live camera streams. You can switch between these modes using the bottom bar.
You have the option to either select an image from your device or capture a new photo to classify.
The app will classify a continuous stream of image frames captured by the camera.
