MAP difference between TensorRT 8 and TensorRT10 for YOLOV8 DLA Compilation
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Description
Description
Environment
TensorRT Version: 8.5.2.2
CUDA Version: 11.4.315
CUDNN Version: 8.6.0.166
**Nvidia Jetson Orin NX 16GB
Operating System:
Python Version (if applicable): 3.8.10
Used Ultralytics Jetpack5 latest docker for the testing
Relevant Files
Model link:
yolovm8 custom trained model for 3 classes
Have you tried the latest release?:
Attach the captured .json and .bin files from TensorRT's API Capture tool if you're on an x86_64 Unix system
Can this model run on other frameworks? For example run ONNX model with ONNXRuntime (polygraphy run <model.onnx> --onnxrt):
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reproducing the YOLOv8 custom-model validation on the stated Orin NX setup with TensorRT 8.5.2.2 and compare it with the TensorRT 10 result. Run the ONNX model with ONNXRuntime as suggested, and collect the model, validation details, and TensorRT API capture files if available. Done means identifying whether the near-zero MAP is caused by the model, validation path, or TensorRT/DLA version difference.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- embedded-iot, machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100