Please post working tflite_convert command line examples - tensorflow2.0

Question

I am converting several models from Tensorflowsj Keras and Tensorflow to TensorflowLite and then to TensorflowMicro c-header files.
I can do the main conversions but have found little information about using tflite_convert for quantization.
Wondering if people could post working command line examples. As far as I can tell we are encouraged to use python to do the conversions, but I would prefer to stay on the command line.
I have summarized what I am working on here https://github.com/hpssjellis/my-examples-for-the-arduino-portentaH7/tree/master/m09-Tensoflow/tfjs-convert-to-arduino-header.
This is what I have so far and it works converting a saved tensorfowjs model.json into a .pb file that is converted to a .tflite and then to a c-header file to work on an Arduino style microcontroller.
tensorflowjs_converter --input_format=tfjs_layers_model --output_format=keras_saved_model ./model.json ./
tflite_convert --keras_model_file ./ --output_file ./model.tflite
xxd -i model.tflite model.h
But my files do not get any smaller when I try any quantization.
The tflit_convert command line help at Tensorflow is not specific enough https://www.tensorflow.org/lite/convert/cmdline
Here are some examples I have found using both tflite_convert or tensorflowjs_convert, some seem to work on other peoples models but do not seem to work on my own models:
tflite_convert --output_file=/home/wang/Downloads/deeplabv3_mnv2_pascal_train_aug/optimized_graph.tflite --graph_def_file=/home/wang/Downloads/deeplabv3_mnv2_pascal_train_aug/frozen_inference_graph.pb --inference_type=FLOAT --inference_input_type=QUANTIZED_UINT8 --input_arrays=ImageTensor --input_shapes=1,513,513,3 --output_arrays=SemanticPredictions –allow_custom_ops
tflite_convert --graph_def_file=<your_frozen_graph> \
--output_file=<your_chosen_output_location> \
--input_format=TENSORFLOW_GRAPHDEF \
--output_format=TFLITE \
--inference_type=QUANTIZED_UINT8 \
--output_arrays=<your_output_arrays> \
--input_arrays=<your_input_arrays> \
--mean_values=<mean of input training data> \
--std_dev_values=<standard deviation of input training data>
tflite_convert --graph_def_file=/tmp/frozen_cifarnet.pb \
--output_file=/tmp/quantized_cifarnet.tflite \
--input_format=TENSORFLOW_GRAPHDEF \
--output_format=TFLITE \
--inference_type=QUANTIZED_UINT8 \
--output_arrays=CifarNet/Predictions/Softmax \
--input_arrays=input \
--mean_values 121 \
--std_dev_values 64
tflite_convert
--graph_def_file=frozen_inference_graph.pb
--output_file=new_graph.tflite
--input_format=TENSORFLOW_GRAPHDEF
--output_format=TFLITE
--input_shape=1,600,600,3
--input_array=image_tensor
--output_array=detection_boxes,detection_scores,detection_classes,num_detections
--inference_type=QUANTIZED_UINT8
--inference_input_type=QUANTIZED_UINT8
--mean_values=128 \
--std_dev_values=127
tflite_convert --graph_def_file=~YOUR PATH~/yolov3-tiny.pb --output_file=~YOUR PATH~/yolov3-tiny.tflite --input_format=TENSORFLOW_GRAPHDEF --output_format=TFLITE --input_shape=1,416,416,3 --input_array=~YOUR INPUT NAME~ --output_array=~YOUR OUTPUT NAME~ --inference_type=FLOAT --input_data_type=FLOAT
tflite_convert \ --graph_def_file=built_graph/yolov2-tiny.pb \ --output_file=built_graph/yolov2_graph.lite \ --input_format=TENSORFLOW_GRAPHDEF \ --output_format=TFLITE \ --input_shape=1,416,416,3 \ --input_array=input \ --output_array=output \ --inference_type=FLOAT \ --input_data_type=FLOAT
tflite_convert --graph_def_file=frozen_inference_graph.pb --output_file=optimized_graph.lite --input_format=TENSORFLOW_GRAPHDEF --output_format=TFLITE --input_shape=1,1024,1024,3 --input_array=image_tensor --output_array=Softmax
tensorflowjs_converter --quantize_float16 --input_format=tf_hub 'https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/classification/1' ./
tensorflowjs_converter --control_flow_v2=True --input_format=tf_hub --quantize_uint8=* --strip_debug_ops=True --weight_shard_size_bytes=4194304 --output_node_names='Postprocessor/ExpandDims_1,Postprocessor/Slice' --signature_name 'serving_default' https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2 test
If anyone has working examples of quantization that they can explain especially what is important to include and what is optional, that would be very helpful. I use netron to visualize the models so I should be able to see when a float input has been changed to an int8. A bit of an explanation would be helpful to.
Recently tried this set of commands to make which compiled but the quantized file was larger than the un-quantized file
tensorflowjs_converter --input_format=tfjs_layers_model --output_format=keras_saved_model ./model.json ./saved_model
tflite_convert --keras_model_file ./saved_model --output_file ./model.tflite
xxd -i model.tflite model.h
tflite_convert --saved_model_dir=./saved_model \
--output_file=./model_int8.tflite \
--input_format=TENSORFLOW_GRAPHDEF \
--output_format=TFLITE \
--inference_type=QUANTIZED_UINT8 \
--output_arrays=1,1 \
--input_arrays=1,2 \
--mean_value=128 \
--std_dev_value=127
xxd -i model_int8.tflite model_int8.h

Answer 1

The python way is easy as well. And you can find official examples here:
https://www.tensorflow.org/lite/performance/post_training_quantization
There is an entire section for this. I think you didn't train the model so post-training quantization is what you are looking for.