but ng network is depicted in ined using the DistBe- ing system using mod- rallelism. Although we n only, a rough estimate work could be trained to GPUs within a week, the usage. Our training used escent with 0.9 momen- ule (decreasing the learn- lyak averaging [13] was at inference time. e changed substantially ompetition, and already with other options, some- d hyperparameters, such Therefore, it is hard to most effective single way ate matters further, some on smaller relative crops, [8]. Still, one prescrip- y well after the competi- sized patches of the im- y between 8% and 100% constrained to the inter- photometric distortions to combat overfitting to data. fication Challenge n challenge involves the ne of 1000 leaf-node cat- There are about 1.2 mil- r validation and 100,000 is associated with one mance is measured based predictions. Two num- -1 accuracy rate, which the first predicted class, mpares the ground truth es: an image is deemed input Conv 7x7+2(S) MaxPool 3x3+2(S) LocalRespNorm Conv 1x1+1(V) Conv 3x3+1(S) LocalRespNorm MaxPool 3x3+2(S) Conv 1x1+1(S) Conv 1x1+1(S) Conv 1x1+1(S) MaxPool 3x3+1(S) DepthConcat Conv 3x3+1(S) Conv 5x5+1(S) Conv 1x1+1(S) Conv 1x1+1(S) Conv 1x1+1(S) Conv 1x1+1(S) MaxPool 3x3+1(S) DepthConcat Conv 3x3+1(S) Conv 5x5+1(S) Conv 1x1+1(S) MaxPool 3x3+2(S) Conv 1x1+1(S) Conv 1x1+1(S) Conv 1x1+1(S) MaxPool 3x3+1(S) DepthConcat Conv 3x3+1(S) Conv 5x5+1(S) Conv 1x1+1(S) Conv 1x1+1(S) Conv 1x1+1(S) Conv 1x1+1(S) MaxPool 3x3+1(S) AveragePool 5x5+3(V) DepthConcat Conv 3x3+1(S) Conv 5x5+1(S) Conv 1x1+1(S) Conv 1x1+1(S) Conv 1x1+1(S) Conv 1x1+1(S) MaxPool 3x3+1(S) DepthConcat Conv 3x3+1(S) Conv 5x5+1(S) Conv 1x1+1(S) Conv 1x1+1(S) Conv 1x1+1(S) Conv 1x1+1(S) MaxPool 3x3+1(S) DepthConcat Conv 3x3+1(S) Conv 5x5+1(S) Conv 1x1+1(S) Conv 1x1+1(S) Conv 1x1+1(S) Conv 1x1+1(S) MaxPool 3x3+1(S) AveragePool 5x5+3(V) DepthConcat Conv 3x3+1(S) Conv 5x5+1(S) Conv 1x1+1(S) MaxPool 3x3+2(S) Conv 1x1+1(S) Conv 1x1+1(S) Conv 1x1+1(S) MaxPool 3x3+1(S) DepthConcat Conv 3x3+1(S) Conv 5x5+1(S) Conv 1x1+1(S) Conv 1x1+1(S) Conv 1x1+1(S) Conv 1x1+1(S) MaxPool 3x3+1(S) DepthConcat Conv 3x3+1(S) Conv 5x5+1(S) Conv 1x1+1(S) AveragePool 7x7+1(V) FC Conv 1x1+1(S) FC FC SoftmaxActivation softmax0 Conv 1x1+1(S) FC FC SoftmaxActivation softmax1 SoftmaxActivation softmax2 Figure 3: GoogLeNet network with all the bells and whistles. ⼤規模画像認識コンペティション ILSVRCで2014年に優勝 C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In CVPR, 2015.
yukinaga
quiver
brainpadpr
ks91
tomoaki0705
signer
sip3ristex
chalasr
tagtag
hachama
floriandoerfler
lcolladotor
jmmastey
pedronauck
marcduiker
honnibal
aki_iinuma
62gerente
bcantrill
holman
brad_frost
csswizardry
lynnandtonic
colly
