Megvii-BaseDetection / Megvii-BaseDetection/YOLOX
Performance compared to Yolo4Tiny using TensorRT
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Description
Hello,
We were profiling the performance of YoloX-Tiny by comparing inference time to that of Yolo4-Tiny. This was conducted using a 416 px + FP16 configuration for both models, using TensorRT framework.
Oddly enough, the results shown that Yolo4Tiny is around 40% faster than YoloX-Tiny. This number was similar on multiple machines with different HW. Is this the expected result, or did we possibly make some mistake along the way (e.g. when exporting)?
CUDA Version = 11.1 & 10.2
TensorRT Version = 7.2 & 7.1
HW = Jetson AGX Xavier & RTX 2060
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First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
Start by reviewing the TensorRT export and benchmarking setup for YOLOX-Tiny and YOLO4-Tiny at 416 px with FP16. Reproduce the comparison on the listed Jetson AGX Xavier or RTX 2060 configurations, then determine whether the roughly 40% performance gap is expected or indicates an export or measurement issue.
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Assessment
- Tech stack
- python
- Domain
- machine-learning, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 30/100