- Read more about training an image classification model using AutoML Vision Edge model
- Read more about hosting models with Firebase Model deployment
This ML Kit Quickstart App demonstrates how to label images and objects with an AutoML Vision Edge trained model, and also how to download models that are hosted within Firebase.
Features that are included in this demo app:
- Custom Image labeling - Label images using a custom AutoML VisionEdge trained model that is downloaded from Firebase.
- Custom Object Detection and Tracking - Detect and label object using a custom AutoML VisionEdge trained model that is downloaded from Firebase.
- In order to setup this app to run, you need to create a Firebase project as outlined here.
- Run the sample code on your Android device or emulator
- Try extending the code to add new features and functionality
- Ensure your device is connected to the Internet to download the remote model
This app supports three usage scenarios: Live Camera, Static Image, and CameraX enabled live camera.
This mode uses the camera preview as input and allows you to label images using a remotely downloaded AutoML Vision Edge model. There is also a settings page that allows you to configure several options:
- Camera
- Preview Size -- Specify the preview size of rear camera manually (Default size is chose appropriately based on screen size)
- Enable live viewport -- Prevent the live camera preview from being blocked by API rendering speed.
- AutoML Image Labeling
- AutoML Remote Model Name -- Allows you to specify an AutoML VisionEdge model to remotely download from the Firebase Console
The static image scenario is identical to the live camera scenario, but instead relies on images fed into the app through the gallery.
The CameraX live preview scenario is very similar to the native live camera scenario, but instead relies on CameraX live preview instead of the Camera2 live preview. Note: CameraX is only supported on API level 21 .
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http://www.apache.org/licenses/LICENSE-2.0
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