coremltools_tensorflow_mobilenetv2_to_mlpackage_conversion.py

1import tensorflow as tf
2import coremltools as ct
3
4# 1. Load a pre-trained TensorFlow model
5# In this example, we use MobileNetV2 from Keras applications
6tf_model = tf.keras.applications.MobileNetV2(
7    weights="imagenet", 
8    input_shape=(224, 224, 3)
9)
10
11# 2. Define the input type (Image) for the Core ML model
12# This allows the model to accept an image instead of a raw multi-dimensional array
13image_input = ct.ImageType(
14    name="input_1",
15    shape=(1, 224, 224, 3),
16    scale=1.0/127.5, 
17    bias=[-1.0, -1.0, -1.0]
18)
19
20# 3. Convert the model to Core ML
21model = ct.convert(
22    tf_model,
23    inputs=[image_input],
24    classifier_config=ct.ClassifierConfig("https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt")
25)
26
27# 4. Save the converted model
28model.save("MobileNetV2.mlpackage")
29
30print("Model converted and saved successfully as MobileNetV2.mlpackage")