How To Convert Just A H5 File To A Tflite File?

I'm trying to run license plate detection on Android. So first of all I find this tutorial: https://medium.com/@quangnhatnguyenle/detect-and-recognize-vehicles-license-plate-with-m

Solution 1:

TensorFlow Lite model incorporates both weights and model code itself. You need to load Keras model(with weights) and then you will be able to convert into tflite model.

Get a copy of authors' repo, and execute get-networks.sh. You need only data/lp-detector/wpod-net_update1.h5 for license plates detector so you can stop download earlier.

Dive a bit into code and you can find prepared load model function at keras utils.

After you get a model object, you can convert it into tflite.

Python3, TF2.4 tested:

import sys, os
import tensorflow as tf
import traceback

from os.path                    import splitext, basename

print(tf.__version__)

mod_path = "data/lp-detector/wpod-net_update1.h5"defload_model(path,custom_objects={},verbose=0):
    #from tf.keras.models import model_from_json

    path = splitext(path)[0]
    withopen('%s.json' % path,'r') as json_file:
        model_json = json_file.read()
    model = tf.keras.models.model_from_json(model_json, custom_objects=custom_objects)
    model.load_weights('%s.h5' % path)
    if verbose: print('Loaded from %s' % path)
    return model

keras_mod = load_model(mod_path)

converter = tf.lite.TFLiteConverter.from_keras_model(keras_mod)
tflite_model = converter.convert()

# Save the TF Lite model.with tf.io.gfile.GFile('model.tflite', 'wb') as f:
    f.write(tflite_model)

Good luck!