1.简介

这里写图片描述

2.文档与性能

这里写图片描述

3.网络与模型能力

这里写图片描述
这里写图片描述

4.生态与维护

这里写图片描述

5.框架搭建与应用

这里写图片描述

6.架构

6.1 TensorFlow

这里写图片描述

6.2 Caffe

这里写图片描述

6.3 MXNet

这里写图片描述
这里写图片描述

6.4 Paddle

主要从以下几个方面入手:多机并行架构、多GPU并行架构、sequence序列模型以及大规模稀疏训练;

6.5 CNTK

这里写图片描述

7.总结

1). 有关图像的问题使用caffe很方便,训练只需要写prototxt;
2). Caffe是目前产品化最多的库;
3). 应该多关注TensorFlow,毕竟有Google这样的亲爹;
4). 用TensorBoard去观察训练的状态;
5). 可以了解和学习一下mxnet,对显存利用率高;

Comment and share

Prepare the input data

Because the input data’s formt of MXNet is rec,so we must turn the image into the .rec format,as folow:

First, we prepare some face images data stored in ‘test_face’ file:

step1:build the txt or lst format from the images

Now,we have so many images in hand.Next,we make some changes to them that generate a list or a txt about the images.As for the list or txt,its format as folllows:


To see explicitly,we can see an image below:

Of course, we still need the test.lst or test.txt.

step2:generate the .rec from txt or lst

Under our MXNet root diretory,we can see

so,we can build the rec doc by using ‘im2rec’.

The comman as folows:

Here,we can see three parameters.They are:

the first param: the path of your lst or txt have been build;

the second one: the path of your images;

the third one: the path of your .rec.

OK,so far we have the rec doc of train data,but we still lack the rec of test data.In that,we can generate the test.rec with the same method.Finally,you can get four files:

Good luck!

Continue reading
  • page 1 of 1

zhleternity

A girl loves algorithms about Computer Vision,Deep Learning,and Machine Learning .etc.She likes making some friends in the same camp.


Worked with all algorithms


GuangZhou
Fork me on GitHub