Как работать с результатом org.tensorflow.lite.Interpreter.runForMultipleInputsOutputs()

Определение выходных данных, подобных следующему, позволяет вам работать с собственными массивами Java, чего я и хотел:

out1 = new float[1][14][14][17];
out2 = new float[1][14][14][34];
out3 = new float[1][14][14][32];
out4 = new float[1][14][14][32];
Map<Integer, Object> outputs = new HashMap<>();
outputs.put(0, out1);
outputs.put(1, out2);
outputs.put(2, out3);
outputs.put(3, out4);

// The shape of *1* output's tensor
int[] OutputShape;
// The type of the *1* output's tensor
DataType OutputDataType;
// The multi-tensor ready storage
outputProbabilityBuffers = new HashMap<>();

ByteBuffer x;
// For each model's tensors (there are getOutputTensorCount() of them for this tflite model)
for (int i = 0; i < tflite.getOutputTensorCount(); i++) {
    OutputShape = tflite.getOutputTensor(i).shape();
    OutputDataType = tflite.getOutputTensor(i).dataType();
    x = TensorBuffer.createFixedSize(OutputShape, OutputDataType).getBuffer();
    outputProbabilityBuffers.put(i, x);
    LOGGER.d("Created a buffer of %d bytes for tensor %d.", x.limit(), i);
}

LOGGER.d("Created a tflite output of %d output tensors.", outputProbabilityBuffers.size());

Пример вывода:

Classifier: Created a buffer of 11264 bytes for tensor 0.
Classifier: Created a buffer of 11264 bytes for tensor 1.
Classifier: Created a buffer of 4 bytes for tensor 2.
Classifier: Created a buffer of 11264 bytes for tensor 3.
Classifier: Created a tflite output of 4 output tensors.

И используйте его таким образом:

Object[] inputs = { your_regular_input };
tflite.runForMultipleInputsOutputs(inputs, outputProbabilityBuffers);

Выход: https://www.tensorflow.org/lite/models/object_detection/overview#output

val locations = outputs.getValue(0).asFlowArray(),
val classes = outputs.getValue(1).asFlowArray(),
val scores = outputs.getValue(2).asFlowArray(),
val detections = outputs.getValue(3).asFlowArray()