There is a superb visualization tool that is called Netron . I used your .tflite file and the input of your model is:

So at your code at line where you calculate bytebuffer

1 * d.inputSize * d.inputSize * 3 * numBytesPerChannel

you have to input

1* 320 * 320 * 3 * 1

the last “1” is for uint8….if you had floats you should put “4”.

After I change TensorImage DataType from UINT8 to FLOAT32, it works.

 val tfImageBuffer = TensorImage(DataType.UINT8)
 ->
 val tfImageBuffer = TensorImage(DataType.FLOAT32)

I’m trying to run my own custom model for object detection. I created my dataset from Google cloud – Vision (https://console.cloud.google.com/vision/) (I boxed and labeled the images) and it looks like this:

After training the model, I downloaded the TFLite files (labelmap.txt, model.tflite and a json file) from here:

Then, I added them to the Android Object Detection example ( https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection/android ) .

But when I run the project it crashes:

2020-07-12 18:03:05.160 14845-14883/? E/AndroidRuntime: FATAL EXCEPTION: inference
    Process: org.tensorflow.lite.examples.detection, PID: 14845
    java.lang.IllegalArgumentException: Cannot copy to a TensorFlowLite tensor (normalized_input_image_tensor) with 307200 bytes from a Java Buffer with 4320000 bytes.
        at org.tensorflow.lite.Tensor.throwIfSrcShapeIsIncompatible(Tensor.java:423)
        at org.tensorflow.lite.Tensor.setTo(Tensor.java:189)
        at org.tensorflow.lite.NativeInterpreterWrapper.run(NativeInterpreterWrapper.java:154)
        at org.tensorflow.lite.Interpreter.runForMultipleInputsOutputs(Interpreter.java:343)
        at org.tensorflow.lite.examples.detection.tflite.TFLiteObjectDetectionAPIModel.recognizeImage(TFLiteObjectDetectionAPIModel.java:197)
        at org.tensorflow.lite.examples.detection.DetectorActivity$2.run(DetectorActivity.java:182)
        at android.os.Handler.handleCallback(Handler.java:883)
        at android.os.Handler.dispatchMessage(Handler.java:100)
        at android.os.Looper.loop(Looper.java:214)
        at android.os.HandlerThread.run(HandlerThread.java:67)

I tried changing the parameters TF_OD_API_IS_QUANTIZED to false and labelOffset to 0, and also I modified this line from the TFLiteObjectDetectionAPIModel.java to d.imgData = ByteBuffer.allocateDirect(_4_ * d.inputSize * d.inputSize * 3 * numBytesPerChannel); (I replaced 1 for 4)

I am new to this, I would really appreciate if someone could help me understand and resolve the error. Thank you!

Update:
Here are the tflite files : https://drive.google.com/drive/folders/11QT8CgaYF2EseORgGCceh4DT80_pMiFM?usp=sharing (I don’t care if the model recognize correctly the squares and circles, I just want to check if it compiles on the android app and then I will improve it)