Hello, thank you for helping.
I went through the jetson inference repo, but I can’t seem to find the option for yolov5 model.
I am also encountering some errors while trying to accomplish my objective using deepstream.
Can you please suggest if I can use the first method I mentioned in my issue, to use the csi-camera code for the yolo model? If it is possible, can you please help me understand how can I do it?
Hello @AastaLLL ,
Okay I understood from your suggested link where I can pass this frame. But in the link, the /usr/src/tensorrt/bin/trtexec --onnx=/usr/src/tensorrt/data/resnet50/ResNet50.onnx --saveEngine=sample.engine the instruction is for resnet-50. Can you please tell me how can I modify it for yolov5 weights?
Hello @AastaLLL ,
I have converted my weights to ONNX format. But I am unable to figure how to run yolov5 model instead of Resnet50. As mentioned your suggested link, I tried to navigate to the /usr/src/tensorrt/data directory, to change the Resnet50 to Yolov5, but I couldn’t find yolov5 in the options. Please tell me how to run yolov5 like Resnet50 using this code /usr/src/tensorrt/bin/trtexec --onnx=/usr/src/tensorrt/data/resnet50/ResNet50.onnx --saveEngine=sample.engine.
UPDATE: I ran the code for building the engine using my yolov5 onnx weights. I have built a sample engine. Now when I try to run the code mentioned in your link, on a python terminal, I get this error for tesorrt.
Although I have duely followed all steps and installed tensorrt on my jetson nano.
We only support the default python version. For JetPack4, it’s python 3.6.
For other python versions, you can build it from the source:
You can infer the model with the sample shared above.
For YOLOv5, you will need some post-processing to convert the output tensor into bounding boxes.
You can check the author’s code or some community implementation for more info.