Could not find output coverage layer for parsing objects

[ljay189]

I am not 100% certain on this, but my guess is that you need to implement your own custom bounding box parser in C++. Out of the box, deepstream supports the following models in terms of custom bounding box implementation (source: Using a Custom Model with DeepStream — DeepStream 6.3 Release documentation).

• FasterRCNN
• MaskRCNN
• SSD
• YoloV3 / YoloV3Tiny / YoloV2 / YoloV2Tiny
• DetectNet

You need to write your own custom bbox parsing implementation, compile it to .so for linking and then include the path in the configuration file as follows:

[property]
...
parse-bbox-func-name=NvDsInferParseCustomModel
custom-lib-path=nvdsinfer_custom_impl_model/libnvdsinfer_custom_impl_model.so

See the following post for more information:

Python binding example of parse-bbox-func-name

HI i am changing the sample deepstream_test_1.py I am using the python bindings as i dont know cpp. I have used an ONNX model i have trained using custom vision. do i need to supply a parse function to the config for use as a detector? Where can i see an example of a bounding box parser in python? do you have any configs for onnx that show how to use custome onnx models against the deepstream_test_1.py sample. Do i need an so in this use case? Thanks in advance, and i can post any of the…