Do you speed up by TensorRT ? about yolov5 HOT 18 CLOSED

We have updated the yolov5 tensorrt according to the v2.0 release of this repo.

And made speed test on my machine.

please find https://github.com/wang-xinyu/tensorrtx.

@glenn-jocher could you also add a link to https://github.com/wang-xinyu/tensorrtx in your Tutorials section?

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Hi @glenn-jocher

I just implemented yolov5-s in my repo https://github.com/wang-xinyu/tensorrtx/tree/master/yolov5
, and test on my machine. yolov5-m, yolov5-l, etc, will come out soon.

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@wang-xinyu did a great TensorRT implementation of our https://github.com/ultralytics/yolov3 repo here (which supports both YOLOv3 and YOLOv4), he might best answer this question.
https://github.com/wang-xinyu/tensorrtx/tree/master/yolov3-spp

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Update! My tensorrt implementation already updated according to this commit 364fcfd

The PANet updated.

Please find my repo https://github.com/wang-xinyu/tensorrtx

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@thancaocuong there is no such file.

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I have a python implementation here, with NMS, https://github.com/TrojanXu/yolov5-tensorrt

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Hello @wu-ruijie, thank you for your interest in our work! Please visit our Custom Training Tutorial to get started, and see our Jupyter Notebook , Docker Image, and Google Cloud Quickstart Guide for example environments.

If this is a bug report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you.

If this is a custom model or data training question, please note that Ultralytics does not provide free personal support. As a leader in vision ML and AI, we do offer professional consulting, from simple expert advice up to delivery of fully customized, end-to-end production solutions for our clients, such as:

• Cloud-based AI systems operating on hundreds of HD video streams in realtime.
• Edge AI integrated into custom iOS and Android apps for realtime 30 FPS video inference.
• Custom data training, hyperparameter evolution, and model exportation to any destination.

For more information please visit https://www.ultralytics.com.

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Tensor core support would be amazing!

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Hi! I tested yolo5-s on cpu by directly running detect.py and the inference speed is only 3 fps.Could you please give me some advice?I want to make it 30 fps at least.

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@sljlp you might want to see 'Running yolov5 on CPU' #37

The default --img-size for detect.py is 640, which you can reduce significantly to get the FPS you are looking for.

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@sljlp one caveat is --img-size must be a multiple of the largest stride, 32. So acceptable sizes are 320, 288, 256, etc.

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Update: I've pushed more robust error-checking on --img-size now in 099e6f5, so if a user accidentally requests an invalid size (which is not divisible by 32), the code will warn and automatically correct the value to the nearest valid --img-size.

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@glenn-jocher Can you provide yolov5.weights file. I've found that to convert yolo to tensorrt, we need the weights file to use with (https://github.com/wang-xinyu/tensorrtx/)

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Update! My tensorrt implementation already updated according to this commit 364fcfd

The PANet updated.

Please find my repo https://github.com/wang-xinyu/tensorrtx

Thanks for sharing! Do you have plans to implement other yolov5 versions as well?

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This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.

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@wang-xinyu thanks, yes this is a good idea. Can you submit a PR for the README please?

EDIT: I'll add a link to the export tutorial also.

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https://github.com/ttanzhiqiang/onnx_tensorrt_project

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We have updated the yolov5 tensorrt according to the v2.0 release of this repo.

And made speed test on my machine.

Models Device BatchSize Mode Input Shape(HxW) FPS
YOLOv5-s Xeon E5-2620/GTX1080 1 FP16 608x608 142
YOLOv5-s Xeon E5-2620/GTX1080 4 FP16 608x608 173
YOLOv5-s Xeon E5-2620/GTX1080 8 FP16 608x608 190
YOLOv5-m Xeon E5-2620/GTX1080 1 FP16 608x608 71
YOLOv5-l Xeon E5-2620/GTX1080 1 FP16 608x608 40
YOLOv5-x Xeon E5-2620/GTX1080 1 FP16 608x608 27
please find https://github.com/wang-xinyu/tensorrtx.

@glenn-jocher could you also add a link to https://github.com/wang-xinyu/tensorrtx in your Tutorials section?

Thx for your work, I just wonder how do u test FPS with batchsize.
Cause our video is just one img flow, every img is in a serial line, so why could u use batchsize more than 1?

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