Tflite (TF2) trained model running but not detecting in iOS app - python-3.x

Question

I have trained a pre-model (ssd_mobilenet_v2_fpnlite_640x640) using TF2 Object Detection API then exported to an intermediate SavedModel to then convert it in TFlite model, using the following tutorials:
TF2 Object Detection API, Running TF2 Detection API Models on mobile, Converter Python API guide, and, Edge TF Lite iOS tutorial.
After many work hours I managed to make my model to predict in Python environment and run in the pre-made iOS app from TF lite.
However, after trying many ways of exporting and converting the model I cannot make the model to detect the objects I trained it for to detect.
The following is the instruction for training the model using TF2 API:
python3 model_main_tf2.py \
--pipeline_config_path={pipeline_path}\
--model_dir={output_model_dir} \
--alsologtostderr
This is the instructions for exporting the SavedModel using TF2 API:
python export_tflite_graph_tf2.py \
--pipeline_config_path {pipeline_path} \
--trained_checkpoint_dir {output_model_dir} \
--output_directory {exported_models_dir}
And the following the code to convert the model to tflite from Python API:
converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)
tflite_model = converter.convert()
I have also tried some other alternatives to convert in TF1 like:
converter = tf.compat.v1.lite.TFLiteConverter.from_saved_model(export_dir)
converter.inference_type = tf.compat.v1.lite.constants.QUANTIZED_UINT8
input_arrays = converter.get_input_arrays()
converter.quantized_input_stats = {input_arrays[0] : (0., 1.)} # mean_value, std_dev
tflite_model = converter.convert()
and with command line:
tflite_convert \
--saved_model_dir={saved_model} \
--output_file={output_dir} \
--output_format=TFLITE \
--input_shapes=1,640,640,3 \
--input_arrays='normalized_input_image_tensor' \
--output_arrays='TFLite_Detection_PostProcess','TFLite_Detection_PostProcess:1','TFLite_Detection_PostProcess:2','TFLite_Detection_PostProcess:3' \
--inference_type=QUANTIZED_UINT8 \
--mean_values=128 \
--std_dev_values=127 \
--change_concat_input_ranges=false \
--allow_custom_ops
the result is a 500bytes file. This tflite model looks as follows (in Neutron):
In the IOS app I adjusted the code this way:
// MARK: Model parameters
let batchSize = 1
let inputChannels = 3
let inputWidth = 640
let inputHeight = 640
// image mean and std for floating model, should be consistent with parameters used in model training
let imageMean: Float = 128
let imageStd: Float = 127
I have also tried with some other SSD Mobilenet models unsuccesfully. I've been stuck for several days already, I'd appreciate your help.

Answer 1

Install tf-nighly and convert saved_model to tflite file
https://pypi.org/project/tf-nightly/