Select TensorFlow op(s), included in the given model, is(are) not supported by this interpreter. Make sure you apply/link the Flex delegate before inference. For the Android, it can be resolved by adding "org.tensorflow:tensorflow-lite-select-tf-ops" dependency. See instructions: https://www.tensorflow.org/lite/guide/ops_select

Subject:
Issue with Custom YOLOV5 TFLite Model Integration in Android App

Description:
Hi,

I'm encountering an issue while attempting to integrate a custom YOLOV5 PyTorch model in my Android application using TensorFlow Lite. I've successfully added and used other TensorFlow Lite models for object detection in my app, but when I tried to replace one of these models with my custom YOLOV5 TFLite model, I encountered the following error:

Error Message:

Select TensorFlow op(s), included in the given model, is(are) not supported by this interpreter. Make sure you apply/link the Flex delegate before inference. For Android, it can be resolved by adding "org.tensorflow:tensorflow-lite-select-tf-ops" dependency. See instructions: https://www.tensorflow.org/lite/guide/ops_select

Steps to Reproduce:

1. Added TensorFlow Lite dependencies to my Android app:

   • TensorFlow Lite: implementation 'org.tensorflow:tensorflow-lite:2.8.0'
   • Object Detection: implementation 'org.tensorflow:tensorflow-lite-task-vision:0.3.0'
   • Certain Operations: implementation 'org.tensorflow:tensorflow-lite-select-tf-ops:2.8.0'

2. Converted a custom YOLOV5 PyTorch model to a TFLite model using the following Colab notebook: Link to Colab Notebook

3. Replaced one of the existing TFLite models in my Android application with my custom YOLOV5 TFLite model (named 'diapers-model.tflite').

4. Utilized the following Kotlin code for object detection within my Android app:

class ObjectDetection(private val context: Context) {

    private val IMAGE_WIDTH = 640
    private val IMAGE_HEIGHT = 640
    private val MODEL_PATH = "diapers-model.tflite"


    fun detect(bitmap: Bitmap){

        // Step 1 : Setting Object detection options
        val options = ObjectDetector.ObjectDetectorOptions.builder()
            .setMaxResults(5) // setting max output results
            .setScoreThreshold(0.5f) // set threshold value for detection
            .build()

        // Step 2 : Loading Model
        val detector = ObjectDetector.createFromFileAndOptions(
            context,
            MODEL_PATH,
            options
        )

        // Step 3 : Perform pre-image processing before running inference if needed
        val imageProcessor =  ImageProcessor.Builder()
            // Center crop the image to the largest square possible
            .add( ResizeWithCropOrPadOp(IMAGE_WIDTH , IMAGE_HEIGHT))
        // Resize using Bilinear or Nearest neighbour
        .add(ResizeOp(IMAGE_WIDTH, IMAGE_HEIGHT, ResizeOp.ResizeMethod.BILINEAR))
        .add(NormalizeOp(127.5f, 127.5f))
        // Setting model Quantization
        .add(QuantizeOp(0f, 1/255.0f))
        .build();

        // Step 4 : Creating Tensor Image
        var tensorImage = TensorImage(DataType.FLOAT32) // image input type
        tensorImage.load(bitmap)
        tensorImage = imageProcessor.process(tensorImage)

        // Step 5 : Running
        val result = detector.detect(tensorImage)

        Log.i("ModelResults", "$result")


    }


}

Model Specs:

• Input Shape: [1, 640, 640, 3]
• Input Image Type: Float32

Expected Behavior:
I expect my custom YOLOV5 TFLite model to perform object detection without any errors similar to the other models I've integrated.

Actual Behavior:
I encounter the error mentioned above when attempting to run inference with my custom YOLOV5 TFLite model.

Additional Information:

• Android Studio Version: [Android Studio Giraffe | 2022.3.1 Patch 1]
• Android API Level: [33]

Any assistance or guidance on resolving this issue would be greatly appreciated. Thank you!

The issue provides no repository file or test; start from the Kotlin ObjectDetection.detect entry point, the listed Android dependencies, and diapers-model.tflite. Reproduce the failure in the described Android setup and inspect the model/delegate integration. Done means the custom model runs inference without the unsupported-operation error.