Migrate from the legacy custom model API

Version 22.0.2 of thefirebase-ml-model-interpreter library introduces a newgetLatestModelFile() method, which gets the location on the device of custommodels. You can use this method to directly instantiate a TensorFlow LiteInterpreter object, which you can use instead of theFirebaseModelInterpreter wrapper.

Going forward, this is the preferred approach. Because the TensorFlow Liteinterpreter version is no longer coupled with the Firebase library version, youhave more flexibility to upgrade to new versions of TensorFlow Lite when youwant, or more easily use custom TensorFlow Lite builds.

This page shows how you can migrate from usingFirebaseModelInterpreter to theTensorFlow LiteInterpreter.

1. Update project dependencies

Update your project's dependencies to include version 22.0.2 of thefirebase-ml-model-interpreter library (or newer) and thetensorflow-litelibrary:

Before

implementation("com.google.firebase:firebase-ml-model-interpreter:22.0.1")

After

implementation("com.google.firebase:firebase-ml-model-interpreter:22.0.2")implementation("org.tensorflow:tensorflow-lite:2.0.0")

2. Create a TensorFlow Lite interpreter instead of a FirebaseModelInterpreter

Instead of creating aFirebaseModelInterpreter, get the model's location ondevice withgetLatestModelFile() and use it to create a TensorFlow LiteInterpreter.

Before

Kotlin

valremoteModel=FirebaseCustomRemoteModel.Builder("your_model").build()valoptions=FirebaseModelInterpreterOptions.Builder(remoteModel).build()valinterpreter=FirebaseModelInterpreter.getInstance(options)

Java

FirebaseCustomRemoteModelremoteModel=newFirebaseCustomRemoteModel.Builder("your_model").build();FirebaseModelInterpreterOptionsoptions=newFirebaseModelInterpreterOptions.Builder(remoteModel).build();FirebaseModelInterpreterinterpreter=FirebaseModelInterpreter.getInstance(options);

After

Kotlin

valremoteModel=FirebaseCustomRemoteModel.Builder("your_model").build()FirebaseModelManager.getInstance().getLatestModelFile(remoteModel).addOnCompleteListener{task->valmodelFile=task.getResult()if(modelFile!=null){// Instantiate an org.tensorflow.lite.Interpreter object.interpreter=Interpreter(modelFile)}}

Java

FirebaseCustomRemoteModelremoteModel=newFirebaseCustomRemoteModel.Builder("your_model").build();FirebaseModelManager.getInstance().getLatestModelFile(remoteModel).addOnCompleteListener(newOnCompleteListener<File>(){@OverridepublicvoidonComplete(@NonNullTask<File>task){FilemodelFile=task.getResult();if(modelFile!=null){// Instantiate an org.tensorflow.lite.Interpreter object.Interpreterinterpreter=newInterpreter(modelFile);}}});

3. Update input and output preparation code

WithFirebaseModelInterpreter, you specify the model's input and output shapesby passing aFirebaseModelInputOutputOptions object to the interpreter whenyou run it.

For the TensorFlow Lite interpreter, you instead allocateByteBuffer objectswith the right size for your model's input and output.

For example, if your model has an input shape of [1 224 224 3]float valuesand an output shape of [1 1000]float values, make these changes:

Before

Kotlin

valinputOutputOptions=FirebaseModelInputOutputOptions.Builder().setInputFormat(0,FirebaseModelDataType.FLOAT32,intArrayOf(1,224,224,3)).setOutputFormat(0,FirebaseModelDataType.FLOAT32,intArrayOf(1,1000)).build()valinput=ByteBuffer.allocateDirect(224*224*3*4).order(ByteOrder.nativeOrder())// Then populate with input data.valinputs=FirebaseModelInputs.Builder().add(input).build()interpreter.run(inputs,inputOutputOptions).addOnSuccessListener{outputs->// ...}.addOnFailureListener{// Task failed with an exception.// ...}

Java

FirebaseModelInputOutputOptionsinputOutputOptions=newFirebaseModelInputOutputOptions.Builder().setInputFormat(0,FirebaseModelDataType.FLOAT32,newint[]{1,224,224,3}).setOutputFormat(0,FirebaseModelDataType.FLOAT32,newint[]{1,1000}).build();float[][][][]input=newfloat[1][224][224][3];// Then populate with input data.FirebaseModelInputsinputs=newFirebaseModelInputs.Builder().add(input).build();interpreter.run(inputs,inputOutputOptions).addOnSuccessListener(newOnSuccessListener<FirebaseModelOutputs>(){@OverridepublicvoidonSuccess(FirebaseModelOutputsresult){// ...}}).addOnFailureListener(newOnFailureListener(){@OverridepublicvoidonFailure(@NonNullExceptione){// Task failed with an exception// ...}});

After

Kotlin

valinBufferSize=1*224*224*3*java.lang.Float.SIZE/java.lang.Byte.SIZEvalinputBuffer=ByteBuffer.allocateDirect(inBufferSize).order(ByteOrder.nativeOrder())// Then populate with input data.valoutBufferSize=1*1000*java.lang.Float.SIZE/java.lang.Byte.SIZEvaloutputBuffer=ByteBuffer.allocateDirect(outBufferSize).order(ByteOrder.nativeOrder())interpreter.run(inputBuffer,outputBuffer)

Java

intinBufferSize=1*224*224*3*java.lang.Float.SIZE/java.lang.Byte.SIZE;ByteBufferinputBuffer=ByteBuffer.allocateDirect(inBufferSize).order(ByteOrder.nativeOrder());// Then populate with input data.intoutBufferSize=1*1000*java.lang.Float.SIZE/java.lang.Byte.SIZE;ByteBufferoutputBuffer=ByteBuffer.allocateDirect(outBufferSize).order(ByteOrder.nativeOrder());interpreter.run(inputBuffer,outputBuffer);

4. Update output handling code

Finally, instead of getting the model's output with theFirebaseModelOutputsobject'sgetOutput() method, convert theByteBuffer output to whateverstructure is convenient for your use case.

For example, if you're doing classification, you might make changes like thefollowing:

Before

Kotlin

valoutput=result.getOutput(0)valprobabilities=output[0]try{valreader=BufferedReader(InputStreamReader(assets.open("custom_labels.txt")))for(probabilityinprobabilities){vallabel:String=reader.readLine()println("$label:$probability")}}catch(e:IOException){// File not found?}

Java

float[][]output=result.getOutput(0);float[]probabilities=output[0];try{BufferedReaderreader=newBufferedReader(newInputStreamReader(getAssets().open("custom_labels.txt")));for(floatprobability:probabilities){Stringlabel=reader.readLine();Log.i(TAG,String.format("%s: %1.4f",label,probability));}}catch(IOExceptione){// File not found?}

After

Kotlin

modelOutput.rewind()valprobabilities=modelOutput.asFloatBuffer()try{valreader=BufferedReader(InputStreamReader(assets.open("custom_labels.txt")))for(iinprobabilities.capacity()){vallabel:String=reader.readLine()valprobability=probabilities.get(i)println("$label:$probability")}}catch(e:IOException){// File not found?}

Java

modelOutput.rewind();FloatBufferprobabilities=modelOutput.asFloatBuffer();try{BufferedReaderreader=newBufferedReader(newInputStreamReader(getAssets().open("custom_labels.txt")));for(inti=0;i <probabilities.capacity();i++){Stringlabel=reader.readLine();floatprobability=probabilities.get(i);Log.i(TAG,String.format("%s: %1.4f",label,probability));}}catch(IOExceptione){// File not found?}

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Last updated 2025-12-17 UTC.