I've spent way too long today trying to convert an Object Detection TensorFlow2 model to a CoreML object classifier (with bounding boxes, labels and probability score)
The 'SSD MobileNet v2 320x320' is here: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md
And I've been following all sorts of posts and ChatGPT
https://apple.github.io/coremltools/docs-guides/source/tensorflow-2.html#convert-a-tensorflow-concrete-function
https://developer.apple.com/videos/play/wwdc2020/10153/?time=402
To convert it.
I keep hitting the same errors though, mostly around:
NotImplementedError: Expected model format: [SavedModel | concrete_function | tf.keras.Model | .h5 | GraphDef], got <ConcreteFunction signature_wrapper(input_tensor) at 0x366B87790>
I've had varying success including missing output labels/predictions.
But I simply want to create the CoreML model with all the right inputs and outputs (including correct names) as detailed in the docs here: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_mobile_tf2.md
It goes without saying I don't have much (any) experience with this stuff including Python so the whole thing's been a bit of a headache.
If anyone is able to help that would be great.
FWIW I'm not attached to any one specific model, but what I do need at minimum is a CoreML model that can detect objects (has to at least include lights and lamps) within a live video image, detecting where in the image the object is.
The simplest script I have looks like this:
import coremltools as ct
import tensorflow as tf
model = tf.saved_model.load("~/tf_models/ssd_mobilenet_v2_320x320_coco17_tpu-8/saved_model")
concrete_func = model.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
mlmodel = ct.convert(
concrete_func,
source="tensorflow",
inputs=[ct.TensorType(shape=(1, 320, 320, 3))]
)
mlmodel.save("YourModel.mlpackage", save_format="mlpackage")
