Tensorflow.lite model 在 Android 应用程序中产生错误（不同）结果？

Question

I've made an Image classification model and converted it to tflite format. Then I've verified tflite model in Python using tf.lite.Interpreter — it produces same results for my test image as the original model. Here's a colab link to verify .

Then I embedded it to a sample Android app, using Android Studio ML Model Binding and exact example code from Android studio. Here's the main activity code , you can also use this link to navigate to the full android project.

val assetManager = this.assets
val istr = assetManager.open("test_image.JPG") //The same image
val b = BitmapFactory.decodeStream(istr)

val model = Model2.newInstance(this) //Model definition generated by Android Studio

// Creates inputs for reference.
val image = TensorImage.fromBitmap(b)

// Runs model inference and gets result.
val outputs = model.process(image)
val probability = outputs.probabilityAsCategoryList
probability.sortByDescending { it.score }
val top9 = probability.take(9)

this.findViewById<TextView>(R.id.results_text).text = top9.toString()

And then I'm getting completely different results on Android for the same model and the same input image.

Here are results matching my initial model in Python:

Here are wrong results I'm getting in Android app:

Links to the model and the test image are there in both examples, but I'll post them into the question once again:

tflite model

test image

I guess it has something to do with input/output formats of the model. Or the image is interpreted differently in python and in android. Or the metadata I added to the model is somehow wrong. Anyways, I've tried everything to localize the issue and now I'm stuck.

How do I fix my model or Android code so it produces the same results as my python code?

Answer 1

I've managed to find and fix the issue: My model from this tutorial included a built-in image normalization layer. Image normalization is when you transform standard 0-255 image color values to 0.0-1.0 float values, suitable for machine learning.

But the metadata I used for the tflite model included 2 parameters for external normalization: mean and std. Formula for each value being: normalized_value = (value - mean) / std Since my model handles its own normalization, I need to turn off external normalization by setting mean = 0 and std = 1 . This way I'll get normalized_value = value .

So, setting the tflite metadata parameters to these:

    image_min=0,
    image_max=255.0,
    mean=[0.0],
    std=[1.0]

fixed the double normalization issue and my model now produces correct results in Android app.