keras-yolo4 icon indicating copy to clipboard operation
keras-yolo4 copied to clipboard

GPU memory requirement

Open timezone163 opened this issue 4 years ago • 6 comments

请问训练该模型使用的GPU是什么型号,显存大小? GPU=8G显存,能否支持训练batch_size = 4?

timezone163 avatar May 08 '20 02:05 timezone163

支持不了的

Simon-liusheng avatar May 08 '20 08:05 Simon-liusheng

谢谢您的回答,请问作者使用的是什么显卡,准备入手一款训练用显卡,显存最低要求,谢谢

timezone163 avatar May 09 '20 00:05 timezone163

我现在的就是8g,但是bs只能2,我还想用下百度的显卡,16g,配环境相当的麻烦。

Simon-liusheng avatar May 09 '20 02:05 Simon-liusheng

@Simon-liusheng 8G显存的话,只需要将batch_size = 2就能完成作者的训练吗?model size不要修改吧?比如将(608,608)改为(512, 512),或者(416,416)。 因为我看默认下载的yolov4.weights的model size是(608,608)。 谢谢

timezone163 avatar May 09 '20 04:05 timezone163

@Simon-liusheng 8G显存的话,只需要将batch_size = 2就能完成作者的训练吗?model size不要修改吧?比如将(608,608)改为(512, 512),或者(416,416)。 因为我看默认下载的yolov4.weights的model size是(608,608)。 谢谢

ohh i did not notice that. are we supposed to be using 608X608 in train? I sort of assumed it would not matter if this was from Darknet yolov4. Also I have a RTX 2080 TI founders edition with 11g. It can barely handle a batch size of 4 on a small data set. This seems odd. Why is memory usage so much?

robisen1 avatar May 26 '20 04:05 robisen1

@Simon-liusheng 8G显存的话,只需要将batch_size = 2就能完成作者的训练吗?model size不要修改吧?比如将(608,608)改为(512, 512),或者(416,416)。 因为我看默认下载的yolov4.weights的model size是(608,608)。 谢谢

ohh i did not notice that. are we supposed to be using 608X608 in train? I sort of assumed it would not matter if this was from Darknet yolov4. Also I have a RTX 2080 TI founders edition with 11g. It can barely handle a batch size of 4 on a small data set. This seems odd. Why is memory usage so much?

qqwweee's implemantation is on yolov3 not v4. yolov4 is a way bigger network and requiers a lot more vram to run. In the github repository of the original yolo their is a section witch tells you what changes you need to do according to your gpu memory to train it faster

iliask97 avatar Jul 19 '20 23:07 iliask97