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image_classification

README.md

Neo AI DLR image classification Android example application

Overview

This is an example application for neo-ai-dlr on Android. It uses Image classification models from different frameworks to continuously classify whatever it sees from the device's back camera. Inference is performed using the DLR Java API. The demo app classifies frames in real-time, displaying the top most probable classifications. It allows the user to choose between MobileNet or ResNet models from different frameworks

These instructions walk you through building and running the demo on an Android device. For an explanation of the source, see DLR Android image classification example.

Models

App Uses the following Neo pre-compiled models:

  • gluoncv_mobilenet_v2_075
  • gluoncv_mobilenet_v2_100
  • gluoncv_resnet18_v2
  • gluoncv_resnet50_v2
  • keras_mobilenet_v2
  • tf_mobilenet_v1_100

Other models can be compiled by followng the instructions in Compile Deep Learning Models for Android

Requirements

  • Android Studio 3.2 (installed on a Linux, Mac or Windows machine)

  • Android device in developer mode with USB debugging enabled

  • USB cable (to connect Android device to your computer)

Build and run

Step 1. Clone Neo AI DLR source code

Clone the neo-ai-dlr GitHub repository to your computer to get the demo application.

git clone --recursive https://github.com/neo-ai/neo-ai-dlr.git

cd neo-ai-dlr/examples/android/image_classification

Step 2. Download dlr-release.aar

Download dlr-release.aar file by running

./download-dependencies.sh

dlr-release.aar will be downloaded to dlr-release folder.

Step 3. Download Neo pre-compiled models

Download pre-compiled models by running gradle task downloadModels. Set device arch in app/download.gradle (arm64-v8a, armeabi-v7a, x86_64, x86) before running the command

./gradlew downloadModels

The models will be downloaded and extracted to app assets folder.

Step 4. Open the image_classification project in Android Studio.

To do this, open Android Studio and select Open an existing project, setting the folder to neo-ai-dlr/examples/android/image_classification

Step 5. Build the Android Studio project

Select Build -> Make Project and check that the project builds successfully. You will need Android SDK configured in the settings. You'll need at least SDK version 23. The build.gradle file will prompt you to download any missing libraries.

Note:

`download-dependencies.sh` downloads the latest dlr-release.aar library.

If you see a build error related to compatibility with DLR Java API (for example, `method X is undefined for type DLR`), there has likely been a backwards compatible change to the API. You will need to run `git pull` in the neo-ai-dlr repo to obtain a version that is compatible with the latest dlr-release.aar.

Step 6. Install and run the app

Connect the Android device to the computer and be sure to approve any ADB permission prompts that appear on your phone. Select Run -> Run app. 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.

To test the app, open the app called Neo Classify on your device. 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.

Assets folder

Do not delete the assets folder content. If you explicitly deleted the folders/files, download Neo pre-compiled models as described in Step 3.