Pocket AutoML: Tutorial for Creating an Android App for Image Classification with Deep Learning
APACHE-2.0 License
This document will walk you through the steps for creating your Android app that runs a deep learning image classification model trained in Pocket AutoML and exported in TensorFlow Lite format. The app will continuously classify whatever it sees from the device's back camera.
This tutorial is based on TensorFlow Lite image classification Android example application. For an explanation of its source code, see Explore the code.
If you have any issues following this tutorial please contact me (the creator of Pocket AutoML) via email at or by creating a GitHub issue.
Android Studio 4.2 (installed on a Linux, Mac or Windows machine)
[if not using an Android emulator] an Android device in developer mode with USB debugging enabled and a USB cable (to connect an Android device to your computer)
Install Pocket AutoML from Google Play Store and open it
Create a task e.g. Kittens or Puppies
by pressing +
button
Create a class e.g. Kittens
Add example images of the class by taking photos with a camera or picking them from a storage
Go back to the task view by pressing <-
and repeat these steps for each class
Go back to the task view by pressing <-
, switch to the MODEL
tab and press TRAIN
Press EXPORT IN TENSORFLOW LITE FORMAT
Swipe down on the status bar at the top of the screen to open the notification drawer and track the export progress. The export takes few minutes.
When the export is done, press Share Model
on a notification to open the standard Android Sharesheet, chose a sharing method to send a model to your PC (e.g. send it to yourself via an email app like GMail or store it on your cloud storage like Google Drive or Dropbox)
Run the following command to get the demo application.
git clone https://github.com/OutSorcerer/pocket-automl-android-tutorial
Open the example source code in Android Studio. To do this, open Android
Studio and select Open an existing project
, setting the folder to
pocket-automl-android-tutorial
Unlike the original example, this one uses only TFLite Support library to avoid confusion. An alternative is TensorFlow Lite Task Library, see the README of the original example for details.
Select Build -> Make Project
and check that the project builds successfully.
The build.gradle
file will prompt you to download any missing
libraries.
Follow this step to make sure that the example runs successfully in your environment using its built-in models. The following step will demonstrate how to add your custom model from Pocket AutoML into the example app.
If you are willing to test the app on an Android device, connect the device to the computer and be sure to approve any ADB
permission prompts that appear on your phone. Click Run -> Run 'app'
from the main menu of Android Studio. Select
the deployment target in the connected devices to the device on which the app
will be installed. This will install the app on the device.
If you are willing to test the app on an Android emulator
Tools -> AVD Manager -> Create Virtual Device...
Pixel 2
(this controls its screen resolution and density)Next
and select a system image, Android 11 (API level 30)
is recommended, click Download
on the selected system image, wait for download to complete, click Next
and Finish
AVD Manager
, select the newly created device in a list of available devices and click Run -> Run 'app'
from the main menu of Android StudioIf you want to know more, see Create and manage virtual devices in Android documentation.
To test the app, open the app called Pocket AutoML Classify
on your device or emulator.
When you run the app the first time, the app will request permission to access the camera.
Re-installing the app may require you to uninstall the previous installations.
At this point you must have <your_task_name>.zip
file on your PC. Extract its contents into pocket-automl-android-tutorial/models/src/main/assets
. You will have <your_task_name>.tflite
and <your_task_name>.labels.txt
there.
Open ClassifierPocketAutoML.java
(by clicking Navigate -> Search Everywhere
or pressing Shift
twice and typing its name)
Replace the implementation of getModelPath
with return "<your_task_name>.tflite";
Replace the implementation of getLabelPath
with return "<your_task_name>.labels.txt";
Run the app, swipe up the bottom sheet to expand it and select Pocket_AutoML
from the Model
dropdown menu
You will see the the predicted class and the associated probability as a bold text under an image and probabilities of other classes below. Well done!
Do you have a task at hand that can be solved with a help of an image classification model running in a mobile app? It could be sorting lego bricks or controlling a robot with hand gestures.
I will be excited to know what you have built with the help of Pocket AutoML and this tutorial and will add links to Play Store or GitHub into this document.
TF Lite can run not only on Android but on other platforms as well including iOS, embedded Linux devices like Raspberry Pi or Coral and microcontrollers.
You can try other no-code or low-code deep learning solutions like Teachable Machine, Lobe, Create ML, Google AutoML Vision, Azure Custom Vision or TensorFlow Lite Model Maker.
Pocket AutoML uses transfer learning approach, you can also implement it yourself using a tutorial Transfer learning and fine-tuning in Google Colab.
If you want to learn how to train better models and have a systematic understanding or deep learning I recommend Deep Learning Specialization and Machine Learning Engineering for Production (MLOps) Specialization on Coursera.
TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc. Android is a trademark of Google LLC.