Open DLTensor opened 3 years ago
On some GPUs (such as NVIDIA PASCAL: 1080ti, 1070...), Darknet Group convolution is not well supported, which will cause the problem of low training inference efficiency, but it will not appear on the 20 series and 16 series graphics cards, for example The reasoning time for 2080ti is 2ms, and 1660ti is 3ms. It is suspected to be the cause of CUDNN. It is recommended that users in this situation use pytorch for training and inference
ok,I will try Thanks for your reply
---Original--- From: "dog-qiuqiu"<notifications@github.com> Date: Wed, Sep 16, 2020 23:51 PM To: "dog-qiuqiu/Yolo-Fastest"<Yolo-Fastest@noreply.github.com>; Cc: "DLTensor"<1523452426@qq.com>;"Author"<author@noreply.github.com>; Subject: Re: [dog-qiuqiu/Yolo-Fastest] inference costs 29ms 1080TI (#13)
On some GPUs (such as NVIDIA PASCAL: 1080ti, 1070...), Darknet Group convolution is not well supported, which will cause the problem of low training inference efficiency, but it will not appear on the 20 series and 16 series graphics cards, for example The reasoning time for 2080ti is 2ms, and 1660ti is 3ms. It is suspected to be the cause of CUDNN. It is recommended that users in this situation use pytorch for training and inference
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I followed your README.md, modified Makefile to open CUDA CUDNN and GPU, and then run "make" I used ./Yolo-Fastest/VOC/yolo-fastest.weights to run image_yolov3.sh, it costs 29ms on 1080TI GPU. I do not know where is wrong looking forward to your reply