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https://github.com/iamnabink/tensorflowimageclassificationdemoapp

An demo app of TensorFlow Lite using image classification in Android Studio
https://github.com/iamnabink/tensorflowimageclassificationdemoapp

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An demo app of TensorFlow Lite using image classification in Android Studio

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# TensorFlow Lite image classification Android example application



## Overview



This is an example application for [TensorFlow Lite](https://tensorflow.org/lite)

on Android. It uses

[Image classification](https://www.tensorflow.org/lite/models/image_classification/overview)

to continuously classify whatever it sees from the device's back camera.

Inference is performed using the TensorFlow Lite Java API. The demo app

classifies frames in real-time, displaying the top most probable

classifications. It allows the user to choose between a floating point or

[quantized](https://www.tensorflow.org/lite/performance/post_training_quantization)

model, select the thread count, and decide whether to run on CPU, GPU, or via

[NNAPI](https://developer.android.com/ndk/guides/neuralnetworks).



These instructions walk you through building and

running the demo on an Android device. For an explanation of the source, see

[TensorFlow Lite Android image classification example](EXPLORE_THE_CODE.md).



### Model



We provide 4 models bundled in this App: MobileNetV1 (float), MobileNetV1

(quantized), EfficientNetLite (float) and EfficientNetLite (quantized).

Particularly, we chose "mobilenet_v1_1.0_224" and "efficientnet-lite0".

MobileNets are classical models, while EfficientNets are the latest work. The

chosen EfficientNet (lite0) has comparable speed with MobileNetV1, and on the

ImageNet dataset, EfficientNet-lite0 out performs MobileNetV1 by ~4% in terms of

top-1 accuracy.



For details of the model used, visit [Image classification](https://www.tensorflow.org/lite/models/image_classification/overview).



Downloading, extracting, and placing the model in the assets folder is managed

automatically by download.gradle.



## Requirements



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



*   Android device in

    [developer mode](https://developer.android.com/studio/debug/dev-options)

    with USB debugging enabled



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



## Build and run



### Step 1. Clone the TensorFlow examples source code



Clone the TensorFlow examples GitHub repository to your computer to get the demo

application.



```

git clone https://github.com/tensorflow/examples

```



Open the TensorFlow source code in Android Studio. To do this, open Android

Studio and select `Open an existing project`, setting the folder to

`examples/lite/examples/image_classification/android`






### Step 2. 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.



The file `download.gradle` directs gradle to download the two models used in the

example, placing them into `assets`.









Note:
`build.gradle` is configured to use

TensorFlow Lite's nightly build.
If you see a build error related to

compatibility with Tensorflow Lite's Java API (for example, `method X is

undefined for type Interpreter`), there has likely been a backwards compatible

change to the API. You will need to run `git pull` in the examples repo to

obtain a version that is compatible with the nightly build.



### Step 3. 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 `TFL 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

files, choose `Build -> Rebuild` to re-download the deleted model files into the

assets folder.