Cannot convert tf to onnx in object detection about axelerate

[AIWintermuteAI]

Hi there!
Yes, the onnx converter option was broken by moving to tf 2.0, too many API changes.
I fixed it now, can you check using dev branch?

If running in colab, you will need to replace the first cell with

#we need imgaug 0.4 for image augmentations to work properly, see https://stackoverflow.com/questions/62580797/in-colab-doing-image-data-augmentation-with-imgaug-is-not-working-as-intended
!pip uninstall -y imgaug && pip uninstall -y albumentations && pip install imgaug==0.4 && pip install tf2onnx
!git clone https://github.com/AIWintermuteAI/aXeleRate.git
!cd aXeleRate && git checkout dev
import sys
sys.path.append('/content/aXeleRate')
from axelerate import setup_training, setup_inference

And then run training as usual. I tested all three types of networks (classifier, detector and segnet) on my local computer - they all worked as expected, outputting .onnx file to project folder. I also tested just detector in Colab

Btw, in your config,

"weights" : {
"full": "mobilenet_1_0_224_tf_no_top.h5",

is not supposed to be used like that. For full weights, you're supposed to pass the pass to full weights, normally it is done for resuming the training.

You're passing "no_top_model, which is needs to go to backend.

[arthurkafer]

Thanks for the fast update!

I forgot to change the weights for this test, and now it worked fine.