Nice to meet you. I’m yukihiro from TierIV.
I’m very interest in this work.
We recently had a proposal from TierIV for a new architecture for Autoware.
The following table shows the proposed new architecture, which is currently all unified to TensorRT. However, we are not proposing to use TensorRT for inference in the architecture proposal, so I thought I’d have to think about this a bit more.
You’re right that TensorRT is tied to NVIDIA’s license, which is against OSS policy, so I think TVM is a very good option!
Empirically lidar_apollo_instance_segmentation is more accurate than lidar_point_pillars, so what do you think about working from lidar_apollo_instance_segmentation?
Now we are working on lidar_apollo_instance_segmentation for transfer learning and fine tuning, and it would be great to be able to work with this activity.
- Autoware Foundation Technical Steering Committee Discussion
- Architecture Proposal Presentation Slides
| Node | File Format | Inderence Engine |
|---|---|---|
| lidar_apollo_instance_segmentation | caffe | TensorRT |
| tensorrt_yolo3 | caffe | TensorRT |
| traffic_light_fine_detector | onnx | TensorRT |