晓宇咿呀呀 2019-06-15 15:21
学习一门语言 1、了解(特点、应用场景、历史) 2、目的(完成小程序) 3、安装环境 4、基本语法 5、数据存储(分子原子----二进制) 6、面向对象 7、高级特性 8、框架 9、项目 计算机,利用逻辑语言来模拟现实(分子、原子) 逻辑、动作


飞翔 2019-05-31 17:22

人民币面值识别 热身赛经验分享

小康康 2019-05-31 20:30
RMB面值识别: 先明确这是一个图像分类问题,自然而然地就会想到经典的图像分类网络,比如结构较为简单的VGG,稍微复杂的Resnet和Dense,Inception 明确可以用到的模型后,再来看任务内容,识别人民币面值,从提供的数据集可以看到,其实肉眼是很容易区分开的,说明分类任务其实没那么复杂,经过卷积神经网络提取特征后,类间差异是比较大的,所以可以用Inception解决问题。 我最后采用了I
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

jixiaohui 2018-01-10 14:52
We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

辉仔 2018-01-10 10:49
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the trai

Fully Convolutional Networks for Semantic Segmentation

辉仔 2018-01-10 10:46
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-a

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

jixiaohui 2018-01-10 11:37
Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve ve

Deep Residual Learning for Image Recognition

jixiaohui 2018-01-10 11:15
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly re

Dynamic Routing Between Capsules

dwSun 2018-02-06 16:16
A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or an object part. We use the length of the activity vector

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

辉仔 2018-01-10 11:09
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attenti