标签：des http io ar for 2014 div art sp
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16–19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively.
看一下摘要，就差不多知道作者要讲解的是什么了,depth!!!!!!
Google的模型也是depth啊,所以shuicheng yan的slides里说他们的模型not deep enough！
Very Deep Convolutional Networks for Large-Scale Image Recognition
标签：des http io ar for 2014 div art sp
原文地址：http://www.cnblogs.com/jianyingzhou/p/3976737.html
