StepTurtle
commented
3 weeks ago Here is the results of pre-trained models shared in this link from mmdetection.
Results:
- I used PyTorch models to get these results (models with
.pthextension)
| Model | Score Threshold | NMS Threshold | Detection Time Per Image | Video Link |
|---|---|---|---|---|
| RTMDet-Ins-s | 0.3 | 0.3 | ~20 ms | Video Link |
| RTMDet-Ins-x | 0.3 | 0.3 | ~33 ms | Video Link |
For now, I tested the pre-trained models shared by mmdetection using mmdetection tools. Also they provide a couple of tools to convert .pth models to .onnx and .engine models. I check both of the converted model results and they are looking same and I think we can say their tools clearly convert the models.
Right now I am trying to handle how can use TensorRT models in cpp with TensorRT libraries.
Checklist
Description
We plan to add the RTMDet model in addition to the existing YOLOX model in Autoware Universe. While the YOLOX model is successful in the bounding box task, its instance segmentation layer is weak. We aim to improve instance segmentation results by adding RTMDet model.
Purpose
Our goal is to enhance the lidar image fusion pipeline by adding the RTMDet model to Autoware for image segmentation.
Possible approaches
We can convert pre-trained PyTorch models to ONNX and TensorRT formats, and we can create a ROS 2 package to handle the TensorRT models in Autoware Universe perception to implement them in Autoware.
Definition of done