Megvii-BaseDetection / Megvii-BaseDetection/YOLOX
YOLOX Inference Speed on RTX 3060 Ti
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- Dominant language
- Python
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Description
Has anyone tested inferencing with YOLOX on pytorch using a RTX 3060 Ti. I only achieved an inference speed of 0.8-0.9 seconds (around 1 fps) . Just wanna double confirm cause I expeceted at least 0.1 seconds (10 fps). Already checked that pytorch is installed correctly and CUDA driver is enabled.
Further information:
CUDA 11.2
Pytorch 1.91
Running inference with default settings shown in demo:
yolox_s - 0.2243s
yolox_m - 0.51s
yolox_l - 0.8635
Contributor guide
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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 with the YOLOX demo's default inference settings and compare the reported timings for yolox-s, yolox-m, and yolox-l on the RTX 3060 Ti. Check whether the CUDA 11.2 and PyTorch 1.91 environment reproduces the observed speeds and identify why they differ from the expected 0.1 seconds; done means an explained discrepancy or a confirmed performance issue.
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Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100