I am trying to use MO on a re-trained model (I followed this tutorial:https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-W...)
There was an error using my model with raspberry pi3 + ncs2 after converting from faster_rcnn_inception_v2_coco to IR
"terminate called after throwing an instance of 'InferenceEngine::details::InferenceEngineException'
what(): Dims and format are inconsistent."
I using Openvino 2019 R2 for pc and Openvino 4.1.2 for raspberry.
What should I do?
Hi Thien Tran,
Thank you posting your issue.
Could you please answer the following:
- Which command did you use to convert the model to IR?
- Did you mean OpenCV version 4.1.2?
- Are you using the latest OpenVINO version?
- Did you manage to run the example in your pc with the older OpenVINO 2019 R2 version?
As the example you are trying to run is not an officially tested example, try to run our Object Detection Faster R-CNN C++ Demo and let us if it works.
I am having the same issue. I'm trying to use different versions of OpenVINO, and different ways to convert my model to IR, however, I have the same answer: Dims and format are inconsistent. I am using Raspberry Pi 3 with other .xml and .bin files, and it is working fine.
Thank you very much in advance for your help.
I am having the same issue.
- I tried different ways to optimized my model to use it with Movidus in the Raspberry Pi 3. In general, this is the code used: sudo python3 ./mo_tf.py --input_model [my_path]/frozen_inference_graph.pb --tensorflow_object_detection_api_pipeline_config [my_path]/object_detection/training/pipeline.config --output=detection_boxes,detection_scores,num_detections --output_dir [output_path] --tensorflow_use_custom_operations_config extensions/front/tf/faster_rcnn_support_api_v1.10.json
- I am using OpenCV 4.2.0-openvino
- I was using OpenVino 2020 but it was not possible to use the samples, so I changed to OpenVino 2019.
- Yes, I can run different samples made by Caffe and Tensorflow. It is working corretly. Not for mine.
Thank you very much in advance.