Incorrect Class IDs while exporting to OpenVINO for NCS2

[Averen19]

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Question

Hi, I have trained a custom dataset and would like to deploy on the NCS2 using Raspberry Pi. I converted the .pt file to .xml with the python export.py --include openvino code. However, my class_ids name are wrong as it still shows the original class ids like people and bicycle for class ids 0 and 1. Any solution?

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[github-actions]

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[glenn-jocher]

@Averen19 if you are using detect.py or val.py with a custom dataset and exported model you can pass your data.yaml as an argument to read your class names from:

yolov5/detect.py

Line 215 in 9b771a3

parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')

[Averen19]

@glenn-jocher Thank you, that problem has been solved. Do you know any resources or code that can do the inferencing using the openVINO toolkit with the converted IR files?
I would like to do detect objects using the NCS 2 on the Raspberry Pi 4.

[github-actions]

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

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