Hi there, my system configuration is is follows:
• Hardware Platform: 1080ti
• DeepStream Version: 6.0
• TensorRT Version: 184.108.40.206
• NVIDIA GPU Driver Version: 470.82.01
• Issue Type: questions
I am trying to create a face recognition application with deepstream 6.0. before recognizing faces, I need to detect them.
I use the “mobilenet0.25_Final.pth” pytorch weights which can be downloaded from here
now, I want to transform the pytorch weight to tensorrt. to do so, I first transform it to onnx using the python code: torch_to_onnx.py which can be downloaded from here
and it works perfectly, giving me a .onnx file at the output. then, I transform the generated .onnx file to tensorrt via the following command:
/usr/src/tensorrt/bin/trtexec --onnx=retina-mobile0.25-288x320.onnx --saveEngine=retina-mobile0.25-288x320.engine
and the engine file “retina-mobile0.25-288x320.engine” is successfully generated at the end.
since this is a custom network, it requires a custom parser function. to do that, I use the custom parser function of this link.
finally, I use the config_detection.txt , and config_main.txt of this link
to perform detection.
the problem however is that detection results using the generated tensorrt engine “retina-mobile0.25-288x320.engine” and the initial pytorch weights “retina-mobile0.25-288x320.onnx” are not the same. in particular, the generated tensorrt engine fails to capture small faces. lets take a look at the detected results:
Result obtained via the generated tensorrt engine:
Results obtained from the initial pytorch weights (disregard the landmarks):
could you kindly provide me with a solution to this?