I am trying to convert fp32 yolo model(trained on custom classes) into an int8 low precision quantized model. However upon conversion I am unable to see any bounding boxes(unlike fp32/fp16) when I try to do inference even though .xml and .bin files are generated. I have tried Default and Accuracy aware training both. I am able to achieve the conversion through command line tools. DL Workbench does not convert to int8 IR also.
How can I tackle the above problem?
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Based on what you are reporting this seems to be related to the OpenVINO™ toolkit, we will move this thread to the proper sub-forum for better assistance and kindly wait for a response.
Intel Customer Support Technician
Thanks for reaching out.
Yolov4 is not validated with OpenVINO. Only yolov1,yolov2 and yolov3 are validated. The information is available in the following page :
The following page contains the list of topologies that have been validated for 8-bit inference feature.
There are three Yolo models (TensorFlow Yolov3,Caffe Yolo v1 tiny and Caffe Yolov3 ) in the above list.
I suggest you use any of these models for post training quantization
Here is an additional reference regarding the workflow of converting a model from FP32 to INT8:
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