Core ML vs TensorFlow Lite Karşılaştırması

import CoreML
import Vision

guard let model = try? VNCoreMLModel(
    for: MyClassifier(configuration: .init()).model
) else { return }

let request = VNCoreMLRequest(model: model) { request, error in
    guard let results = request.results as? [VNClassificationObservation] else { return }
    let top = results.first
    print("Predicted: \(top?.identifier ?? "unknown")")
}

let handler = VNImageRequestHandler(cgImage: capturedImage)
try handler.perform([request])

import org.tensorflow.lite.Interpreter

val tfliteModel = loadModelFile(context, "model.tflite")
val options = Interpreter.Options().apply {
    setUseNNAPI(true)  // Android Neural Networks API
    setNumThreads(4)
}

val interpreter = Interpreter(tfliteModel, options)
val input = ByteBuffer.allocateDirect(1 * 224 * 224 * 3 * 4).order(ByteOrder.nativeOrder())
val output = Array(1) { FloatArray(1000) }

interpreter.run(input, output)
val topIndex = output[0].indices.maxBy { output[0][it] }!!