load custom onnx model, failed to convert bgr8 to rgb8 about ros_deep_learning HOT 6 CLOSED

YOLO isn't explicitly supported in the detectNet code, it would require addition pre/post-processing code to make it work.

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@dusty-nv is there a way to load processed .trt models for the detectnet? I have done the necessary pre/post-processing and converted the onnx file to a TensorRT model

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is there a way to load processed .trt models for the detectnet?

There is, but it is pretty deep in the depths of the code, here: https://github.com/dusty-nv/jetson-inference/blob/12061e0a778ddc237bc153f81465488fe2539742/c/tensorNet.h#L303

That isn't usually used externally, but rather from inside detectNet, imageNet, or segNet when loading a model.

Further, the pre/post-processing I was alluding to wasn't to convert the model to TRT, but the runtime pre/post-processing of the input and output tensors. I am not sure what format YOLO expects (i.e. if mean pixel subtraction/normalization is applied, if it uses BGR or RGB input, as well as how the raw detection results are formatted in the network output)

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@dusty-nv
Ahh, i see. I managed to use the NVIDIA's TensorRT samples to create a ROS node for yolo. Thanks for your help!

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Hi @dusty-nv

So I've read through several issues where you repeatedly clarify this and the jetson-inference repo do not support YOLO. Forgive me for beating a dead horse, but long term is there any chance of a ROS node like this being supported for YOLO inference engine at some point?

For now I can muddle along with my own custom ROS node, but long term I'm curious about NVidia's roadmap. As pointed out above, NVidia gives clear instruction on combining TensorRt and Yolo, so having ROS support with Yolo inference engine would be superb performance-wise (for >>30 FPS object detection use-cases).

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is there any chance of a ROS node like this being supported for YOLO inference engine at some point?

I don't personally plan on creating a ROS node for TensorRT-accelerated YOLO, so you may want to continue with your efforts. It gets to be a lot over time to maintain different detection architectures for training+inference, so I prefer to just to keep SSD-Mobilenet for now instead of the now-numerous YOLO variants.

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