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 using an architecture with very small (3×3) convolution filters, 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. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.
Karen Simonyan. Very Deep Convolutional Networks for Large-Scale Image Recognition. 2014
产品 六年谈 引擎 心理学 战斗公式 手机游戏 手游 数值策划 数值策划文档 海外 游戏 游戏优化 游戏制作 游戏发行 游戏开发 游戏策划 游戏设计 游戏运营 破解 策划 系统策划 网址 聊天 设计模式 项目管理 android ane as3 erlang Excel flash flash性能 flixel haxe java java爬虫新手教程 mysql perl python swf破解 tcp twisted u3d ubuntu unity3d