Integrate TensorFlow Lite models with metadata

TensorFlow Lite metadata contains a rich description of what the model does and how to use the model. It can empower code generators, such as the TensorFlow Lite Android code generator and the Android Studio ML Binding feature, to automatically generates the inference code for you. It can also be used to configure your custom inference pipeline.

Browse TensorFlow Lite hosted models and TensorFlow Hub to download pretrained models with metadata. All image models have been supported.

Generate code with TensorFlow Lite Android code generator

For TensorFlow Lite model enhanced with metadata, developers can use the TensorFlow Lite Android wrapper code generator to create platform specific wrapper code. The wrapper code removes the need to interact directly with ByteBuffer. Instead, developers can interact with the TensorFlow Lite model with typed objects such as Bitmap and Rect.

The usefulness of the code generator depend on the completeness of the TensorFlow Lite model's metadata entry. Refer to the <Codegen usage> section under relevant fields in metadata_schema.fbs, to see how the codegen tool parses each field.

Generate Wrapper Code

You will need to install the following tooling in your terminal:

pip install tflite-support

Once completed, the code generator can be used using the following syntax:

tflite_codegen --model=./model_with_metadata/mobilenet_v1_0.75_160_quantized.tflite \
    --package_name=org.tensorflow.lite.classify \
    --model_class_name=MyClassifierModel \

The resulting code will be located in the destination directory. If you are using Google Colab or other remote environment, it maybe easier to zip up the result in a zip archive and download it to your Android Studio project:

## Zip up the generated code
!zip -r classify_wrapper/

## Kick off the download
from google.colab import files'')

Using the generated code

Step 1: Import the generated code

Unzip the generated code if necessary into a directory structure. The root of the generated code is assumed to be SRC_ROOT.

Open the Android Studio project where you would like to use the TensorFlow lite model and import the generated module by: And File -> New -> Import Module -> select SRC_ROOT

Using the above example, the directory and the module imported would be called classify_wrapper.

Step 2: Update the app's build.gradle file

In the app module that will be consuming the generated library module:

Under the android section, add the following:

aaptOptions {
   noCompress "tflite"

Under the dependencies section, add the following:

implementation project(":classify_wrapper")

Step 3: Using the model

// 1. Initialize the model
MyClassifierModel myImageClassifier = null;

try {
    myImageClassifier = new MyClassifierModel(this);
} catch (IOException io){
    // Error reading the model

if(null != myImageClassifier) {

    // 2. Set the input with a Bitmap called inputBitmap
    MyClassifierModel.Inputs inputs = myImageClassifier.createInputs();

    // 3. Run the model
    MyClassifierModel.Outputs outputs =;

    // 4. Retrieve the result
    Map<String, Float> labeledProbability = outputs.getProbability();

Accelerating model inference

The generated code provides a way for developers to accelerate their code through the use of delegates and the number of threads. These can be set when initiatizing the model object as it takes three parameters:

  • Context: Context from the Android Activity or Service
  • (Optional) Device: TFLite acceleration delegate for example GPUDelegate or NNAPIDelegate
  • (Optional) numThreads: Number of threads used to run the model - default is one.

For example, to use a NNAPI delegate and up to three threads, you can initialize the model like this:

try {
    myImageClassifier = new MyClassifierModel(this, Model.Device.NNAPI, 3);
} catch (IOException io){
    // Error reading the model


Getting ' This file can not be opened as a file descriptor; it is probably compressed'

Under the app module that will uses the library module, insert the following lines under the android section:

aaptOptions {
   noCompress "tflite"

Generate code with Android Studio ML Model Binding

Android Studio ML Model Binding allows you to directly import TensorFlow Lite models and use them in your Android Studio projects. It generates easy-to-use classes so you can run your model with less code and better type safety. See the introduction for more details.

Read the metadata from models

The Metadata Extractor library is a convinient tool to read the metadata and associated files from a models across different platforms (see the Java version and the C++ version is coming soon). Users can also build their own metadata extractor tool in other languages using the Flatbuffers library.

Read the metadata in Java

You can initialize a MetadataExtractor with a ByteBuffer that points to the model:

public MetadataExtractor(ByteBuffer buffer);

The ByteBuffer must remain unchanged for the whole lifetime of the MetadataExtractor. The initialization may fail if the Flatbuffers file identifier of the model metadata does not match the one of the metadata parser. See metadata versioning for more information.

As long as the file identifer is satisfied, the metadata extractor will not fail when reading metadata generated from an old or a future scheme due to the Flatbuffers forward and backwards compatibility mechanism. But fields from future shcemas cannot be extracted by older metadata extractors. The minimum necessary parser version of the metadata indicates the minimum version of metadata parser that can read the metadata Flatbuffers in full. You can use the following method to verify if the minimum necessary parser version is satisfied:

public final boolean isMinimumParserVersionSatisfied();

It is allowed to pass in a model without metadata. However, invoking methods that read from the metadata will cause runtime errors. You can check if a model has metadata by invoking the method:

public boolean hasMetadata();

MetadataExtractor provides convenient functions for you to get the input/output tensors' metadata. For example,

public int getInputTensorCount();
public TensorMetadata getInputTensorMetadata(int inputIndex);
public QuantizationParams getInputTensorQuantizationParams(int inputIndex);
public int[] getInputTensorShape(int inputIndex);
public int getoutputTensorCount();
public TensorMetadata getoutputTensorMetadata(int inputIndex);
public QuantizationParams getoutputTensorQuantizationParams(int inputIndex);
public int[] getoutputTensorShape(int inputIndex);

You can also read associated files through their names with the method:

public InputStream getAssociatedFile(String fileName);

Though the TensorFlow Lite model schema supports multiple subgraphs, the TFLite Interpreter only supports single subgraph so far. Therefore, MetadataExtractor omits subgraph index as an input in its methods.