Migrate from the legacy custom model API

Version 22.0.2 of the firebase-ml-model-interpreter library introduces a new getLatestModelFile() method, which gets the location on the device of custom models. You can use this method to directly instantiate a TensorFlow Lite Interpreter object, which you can use instead of the FirebaseModelInterpreter wrapper.

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

This page shows how you can migrate from using FirebaseModelInterpreter to the TensorFlow Lite Interpreter.

1. Update project dependencies

Update your project's dependencies to include version 22.0.2 of the firebase-ml-model-interpreter library (or newer) and the tensorflow-lite library:

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 a FirebaseModelInterpreter, get the model's location on device with getLatestModelFile() and use it to create a TensorFlow Lite Interpreter.

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>(){
@Override
publicvoidonComplete(@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

With FirebaseModelInterpreter, you specify the model's input and output shapes by passing a FirebaseModelInputOutputOptions object to the interpreter when you run it.

For the TensorFlow Lite interpreter, you instead allocate ByteBuffer objects with 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 values and 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>(){
@Override
publicvoidonSuccess(FirebaseModelOutputsresult){
// ...
}
})
.addOnFailureListener(
newOnFailureListener(){
@Override
publicvoidonFailure(@NonNullExceptione){
// Task failed with an exception
// ...
}
});

After

Kotlin 

valinBufferSize=1*224*224*3*java.lang.Float.SIZE/java.lang.Byte.SIZE
valinputBuffer=ByteBuffer.allocateDirect(inBufferSize).order(ByteOrder.nativeOrder())
// Then populate with input data.
valoutBufferSize=1*1000*java.lang.Float.SIZE/java.lang.Byte.SIZE
valoutputBuffer=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 the FirebaseModelOutputs object's getOutput() method, convert the ByteBuffer output to whatever structure is convenient for your use case.

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

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年09月09日 UTC.