How can I convert the model I trained with Tensorflow (python) for use with TensorflowJS without involving IBM cloud (from the step I'm at now)?

What I'm trying to do

I'm trying to learn TensorFlow object recognition and as usual with new things, I scoured the web for tutorials. I don't want to involve any third party cloud service or web development framework, I want to learn to do it with just native JavaScript, Python, and the TensorFlow library.

What I have so far

So far, I've followed a TensorFlow object detection tutorial (accompanied by a 5+ hour video) to the point where I've trained a model in Tensorflow (python) and want to convert it to run in a browser via TensorflowJS. I've also tried other tutorials and haven't seemed to find one that explains how to do this without a third party cloud / tool and React.

I know in order to use this model with tensorflow.js my goal is to get files like:

• group1-shard1of2.bin
• group1-shard2of2.bin
• labels.json
• model.json

I've gotten to the point where I created my TFRecord files and started training:

py Tensorflow\models\research\object_detection\model_main_tf2.py --model_dir=Tensorflow\workspace\models\my_ssd_mobnet --pipeline_config_path=Tensorflow\workspace\models\my_ssd_mobnet\pipeline.config --num_train_steps=100

It seems after training the model, I'm left with:

• files named checkpoint, ckpt-1.data-00000-of-00001, ckpt-1.index, pipeline.config
• the pre-trained model (which I believe isn't the file that changes during training, right?) ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8

I'm sure it's not hard to get from this step to the files I need, but I honestly browsed a lot of documentation and tutorials and google and didn't see an example of doing it without some third party cloud service. Maybe it's in the documentation, I'm missing something obvious.

The project directory structure looks like this:

Where I've looked for an answer

For some reason, frustratingly, every single tutorial I've found (including the one linked above) for using a pre-trained Tensorflow model for object detection via TensorFlowJS has required the use of IBM Cloud and ReactJS. Maybe they're all copying from some tutorial they found and now all the tutorials include this, I don't know. What I do know is I'm building an Electron.js desktop app and object detection shouldn't require network connectivity assuming the compute is happening on the user's device. To clarify: I'm creating an app where the user trains the model, so it's not just a matter of one time conversion. I want to be able to train with Python Tensorflow and convert the model to run on JavaScript Tensorflow without any cloud API.

So I stopped looking for tutorials and tried looking directly at the documentation at https://github.com/tensorflow/tfjs.

When you get to the section about importing pre-trained models, it says:

Importing pre-trained models

We support porting pre-trained models from:

TensorFlow SavedModel

Keras

So I followed that link to Tensorflow SavedModel, which brings us to a project called tfjs-converter. That repo says:

This repository has been archived in favor of tensorflow/tfjs.

This repo will remain around for some time to keep history but all future PRs should be sent to tensorflow/tfjs inside the tfjs-core folder.

All history and contributions have been preserved in the monorepo.

Which sounds a bit like a circular reference to me, considering it's directing me to the page that just told me to go here. So at this point you're wondering well is this whole library deprecated, will it work or what? I look around in this repo anyway, into: https://github.com/tensorflow/tfjs-converter/tree/master/tfjs-converter

It says:

A 2-step process to import your model:

1. A python pip package to convert a TensorFlow SavedModel or TensorFlow Hub module to a web friendly format. If you already have a converted model, or are using an already hosted model (e.g. MobileNet), skip this step.
2. JavaScript API, for loading and running inference.

And basically says to create a venv and do:

pip install tensorflowjs

tensorflowjs_converter \
    --input_format=tf_saved_model \
    --output_format=tfjs_graph_model \
    --signature_name=serving_default \
    --saved_model_tags=serve \
    /mobilenet/saved_model \
    /mobilenet/web_model

But wait, are the checkpoint files I have a "TensorFlow SavedModel"? This doesn't seem clear, the documentation doesn't explain. So I google it, find the documentation, and it says:

You can save and load a model in the SavedModel format using the following APIs:

Low-level tf.saved_model API. This document describes how to use this API in detail. Save: tf.saved_model.save(model, path_to_dir)

The linked syntax extrapolates somewhat:

tf.saved_model.save(
    obj, export_dir, signatures=None, options=None
)

with an example:

class Adder(tf.Module):
  @tf.function(input_signature=[tf.TensorSpec(shape=[], dtype=tf.float32)])
  def add(self, x):
    return x + x

model = Adder()
tf.saved_model.save(model, '/tmp/adder')

But so far, this isn't familiar at all. I don't understand how to take the results of my training process so far (the checkpoints) to load it into a variable model so I can pass it to this function.

This passage seems important:

Variables must be tracked by assigning them to an attribute of a tracked object or to an attribute of obj directly. TensorFlow objects (e.g. layers from tf.keras.layers, optimizers from tf.train) track their variables automatically. This is the same tracking scheme that tf.train.Checkpoint uses, and an exported Checkpoint object may be restored as a training checkpoint by pointing tf.train.Checkpoint.restore to the SavedModel's "variables/" subdirectory.

And it might be the answer, but I'm not really clear on what it means as far as being "restored", or where I go from there, if that's even the right step to take. All of this is very confusing to someone learning TF which is why I looked for a tutorial that does it, but again, I can't seem to find one without third party cloud services / React.

Please help me connect the dots.