• Hardware Platform (Jetson / GPU)
x86-64 Ubuntu 18.04 machine with Geforce GTX 1660 Super
• DeepStream Version
• TensorRT Version
8.4.2-1+cuda11.6 is what I get when I do
dpkg -l | grep TensorRT, but I have CUDA 11.4
• NVIDIA GPU Driver Version (valid for GPU only)
• Issue Type( questions, new requirements, bugs)
I have seen a lot of articles and posts which say that custom models (specifically in the ONNX format) can be deployed on DeepStream via Graph Composer, but I am unable to find a comprehensive guide on how to do this. I have seen on other questions in this forum which have said to use
nvinfer - however, the linked article doesn’t really explain how to do this.
When trying to figure this out myself, I found the following possible solution: to create an NvDsInferVideo node and input an
nvinfer config file as the parameter
config-file-path. Because of this, I tried to create a text file which would do this for my ONNX model based off a template I found online:
deepstream_custom_nvinfer_config.txt (3.0 KB)
However, when I run my DeepStream graph, no bounding boxes appear. Are there any parameters I’m missing here, and is there a proper tutorial I can follow other than Gst-nvinfer — DeepStream 6.1 Release documentation to learn how to deploy an ONNX model to the DeepStream graph composer?
Edit 1: I found an error with my config file, as I had set network-type=2 when it was supposed to be 0 (my model is object detection). When I did this and ran the graph, I now get an error saying “Could not find output coverage layer for parsing objects” and then “Failed to parse bboxes”. This leads me to believe that I am doing something fundamentally wrong in my attempts to use a custom model - is editing this
nvinfer config file the correct method, and if so, what am I doing wrong?