Analysis of Googlenet,vgg operation performance of eccv2014,imagenet competition in deep learning

These days run Vgg and googlenet really fast be abused cry, Vgg ran 2 weeks to converge to error rate 40%, then change local tyrants K40, run some test results to everyone to see, the first part share performance report, program run in Nvidia K40, video memory 12G, Memory 64G server, training and test data set built in own datasets and imagenet datasets

Training configuration: batchsize=128

Caffe's own imagenet with CuDNN model faster than googlenet with CuDNN

VGG16 layer without CUDNN slower than Caffe own imagenet with CUDNN model

VGG19 layer without CUDNN slower than Caffe own imagenet with CUDNN model

First, CAFFE comes with the configuration, use Cudnn

Forward Speed: 220ms

Backward Speed: 360ms

Second, the CAFFE comes with the configuration, does not use the CUDNN

Forward Speed: 300ms

Backward Speed: 410ms

Third, googlenet, use CUDNN

Forward Speed: 614ms

Backward Speed: 1377ms

Four, googlenet, do not use CUDNN

Forward Speed: 1145ms

Backward Speed: 2009ms

Five, VGG16 layer, use CUDNN

Forward Speed: 3101ms

Backward Speed: 8002ms

Six, VGG19 layer, using CUDNN

Forward Speed: 3972ms

Backward Speed: 8540ms

Here is a question, according to the discussion that Vgg CUDNN will be faster than the use of CUDNN, this need to verify, follow-up can update to everyone, temporary computing resources are scarce, unable to experiment

Also want to say is, vgg convergence extremely slow, do not suggest casually use Vgg do engineering, run a parameter on can, hehe, local tyrants old beauty, anticipate Vgg convergence takes 1 months.

Analysis of Googlenet,vgg operation performance of eccv2014,imagenet competition in deep learning