TFLite-Image

Let’s make machine learning simple.

Building a custom image classifier for your Android application using TensorFlow Lite.

TFLite-Image for Android - TensorFlow Lite inception model image library for Android

Move your trained model to asset folder or prepare a new image inception model using Google teachablemachine machine learning library.

You can use the sample inception quant or float model that we used in this project with 299 image dimension.

• inception_quant.tflite
• inception_float.tflite
• labels.txt
  

Initialization

Add the below repository into your project level build.gradle file.

allprojects {
    repositories {
        ...
        maven { url 'https://jitpack.io' }
    }
}

Add the below dependency into your module level build.gradle file.

dependencies {
    ...
    implementation 'com.github.aslamanver:tflite-image:v1.0.9'
}

Make sure you have added no compress config for your model files

android {
    ....
    aaptOptions {
        noCompress "tflite"
        noCompress "lite"
    }
}

Simple Usage

You need to pass the model file, label text and the model type.

TFLiteImage tfLite = TFLiteImage.getInstance(activity, "your_model_file.tflite", "labels.txt", TFLiteImage.TYPE.QUANT, IMG_DIM_SIZE);
List<Map<String, String>> results = tfLite.predictImage(image view or bitmap image);

IMG_DIM_SIZE is 299 or 224 according to your model, you can visualize your model data to check IMG_DIM_SIZE.

Inception model types

TFLiteImage.TYPE.QUANT
TFLiteImage.TYPE.FLOAT

Use case

TFLiteImage tfLite = TFLiteImage.getInstance(this, "inception_quant.tflite", "labels.txt", TFLiteImage.TYPE.QUANT);
List<Map<String, String>> results = tfLite.predictImage(binding.imgView);
for (Map<String, String> map : results) {
    Log.e("RESULT", map.get("LABEL") + " - " + map.get("CONFIDENCE"));
}

Result

map.get("LABEL");
map.get("CONFIDENCE");

Sunglass - 99%

Glass - 85%

Jeans - 70%

Demonstration

Test the sample app that I made for you: TFLite-Image-v1.0.apk

Made with ❤️ by Aslam Anver