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README.md

TensorFlow Lite Object Detection Android Demo

Object detection

Overview

This is a camera app that continuously detects the objects (bounding boxes and classes) in the frames seen by your device's back camera, using a quantized MobileNet SSD model trained on the COCO dataset. These instructions walk you through building and running the demo on an Android device.

The model files are downloaded via Gradle scripts when you build and run. You don't need to do any steps to download TFLite models into the project explicitly.

Application can run either on device or emulator.

Build the demo using Android Studio

Prerequisites

  • If you don't have already, install Android Studio, following the instructions on the website.
  • You need an Android device and Android development environment with minimum API 21.
  • Android Studio 3.2 or later.

Building

  • Open Android Studio, and from the Welcome screen, select Open an existing Android Studio project.
  • From the Open File or Project window that appears, navigate to and select the /object_detection_demo/02_mobile_app/android directory. Click OK.
  • If it asks you to do a Gradle Sync, click OK.
  • You may also need to install various platforms and tools, if you get errors like Failed to find target with hash string 'android-21' and similar.

Click the Run button (the green arrow) or select Run > Run 'android' from the top menu. You may need to rebuild the project using Build > Rebuild Project.

  • If it asks you to use Instant Run, click Proceed Without Instant Run.
  • Also, you need to have an Android device plugged in with developer options enabled at this point. See here for more details on setting up developer devices.

Model used

Downloading, extraction and placing it in assets folder has been managed automatically by download.gradle.

If you explicitly want to download the model, you can download it from here. Extract the zip to get the .tflite and label file.

Additional Note

Please do not delete the assets folder content. If you explicitly deleted the files, then please choose Build -> Rebuild from menu to re-download the deleted model files into assets folder.

Use your own model

To use your own model:

  • Take existing pre-trained model from the model zoo, test it with demo_1.ipynb script or train your own model on a new dataset (demo_2.ipynb).
  • Create *.tar.gz or *.zip file from detect.tflite with your model and labelmap.txt with your labels. Save *.tar.gz or *.zip to the Google Drive. Note: labelmap.txt file should start with ??? string, because zero position is reserved. So your class labels will start from the 1st position after ??? string.
  • Get downloadable direct link using direct link generator for Google Drive, Dropbox or Onedrive.
  • Replace link in the mobile app download_model.gradle file on your link. Use zipTree for *.zip and tarTree for *.tar.gz. Replace the link
  • Our model is not quantized, so set quantized to false in DetectorActivity.java file Set quantized to false
  • Delete build folder and clean assets folder. Rebuild the project: Build --> Make Project then Build --> Build Bundles --> Build APK.
  • Run mobile app (Run --> Run 'app') in Android Studio and enjoy!