Comments (1)
There are two ways to integrate the TensorRT to this project
- Use the TensorRT-ExecutionProvider provided by ONNX Runtime, this is simple and can be integrated into this project seamlessly.
- Use the vanilla TensorRT-OSS, we can also use ONNX and the TRT Engine exported by YOLOv5.
For the second option, we can also embed the post-processing plugin provided by TRT-OSS with the onnx-graphsurgeon at the end of the exported ONNX graph, we have done some experiments on yolort. And this can reduce the difficulties of deploying. The remaining things that need to do is to implement the pre-processing transformation already available in this project.
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Related Issues (20)
- Segmentation fault (core dumped) HOT 5
- Segmentation fault (core dumped) HOT 1
- Dynamic input shape HOT 2
- fatal error LNK 1104 HOT 2
- OpenCV 4.x ONNXRuntime 1.7+ installation HOT 1
- Add a license to a repository HOT 1
- [Question] Sample with Anchor Box HOT 1
- occur an error in sessionOptions.AppendExecutionProvider_CUDA(cudaOption)
- When run it in clion with args HOT 1
- Support for ONNXRuntime 1.12+ HOT 3
- CUDA failure 101: invalid device ordinal HOT 1
- Invalid input name: �����U HOT 1
- YOLOV5 instance segmentation models
- I encountered a bug in detect HOT 6
- cvtColor does not take effect HOT 1
- Inference speed HOT 7
- Half precision HOT 1
- How to find onnxruntime_cxx_api.h? HOT 4
- ort batch inference HOT 1
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