http://yann.lecun.com/exdb/mnist/

THE MNIST DATABASE

of handwritten digitsYann LeCun, Courant Institute, NYUCorinna Cortes, Google Labs, New YorkChristopher J.C. Burges, Microsoft Research, Redmond

The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.

It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.

Four files are available on this site:

train-images-idx3-ubyte.gz:  training set images (9912422 bytes)
train-labels-idx1-ubyte.gz
training set labels (28881 bytes)


t10k-images-idx3-ubyte.gz:  
test set images (1648877 bytes)


t10k-labels-idx1-ubyte.gz:  
test set labels (4542 bytes)

FILE FORMATS FOR THE MNIST DATABASE

The data is stored in a very simple file format designed for storing vectors and multidimensional matrices. General info on this format is given at the end of this page, but you don't need to read that to use the data files.

All the integers in the files are stored in the MSB first (high endian) format used by most non-Intel processors. Users of Intel processors and other low-endian machines must flip the bytes of the header.

There are 4 files:

train-images-idx3-ubyte: training set images
train-labels-idx1-ubyte: training set labels

t10k-images-idx3-ubyte:  test set images

t10k-labels-idx1-ubyte:  test set labels

The training set contains 60000 examples, and the test set 10000 examples.

The first 5000 examples of the test set are taken from the original
NIST training set. The last 5000 are taken from the original NIST test
set. The first 5000 are cleaner and easier than the last 5000.

TRAINING SET LABEL FILE (train-labels-idx1-ubyte):

[offset] [type]         
[value]          [description]


0000     32 bit integer  0x00000801(2049)
magic number (MSB first)


0004     32 bit integer  60000           
number of items


0008     unsigned byte   ??              
label


0009     unsigned byte   ??              
label


........

xxxx     unsigned byte   ??              
label

The labels values are 0 to 9.

TRAINING SET IMAGE FILE (train-images-idx3-ubyte):

[offset] [type]         
[value]          [description]


0000     32 bit integer  0x00000803(2051)
magic number


0004     32 bit integer  60000           
number of images


0008     32 bit integer  28              
number of rows


0012     32 bit integer  28              
number of columns


0016     unsigned byte   ??              
pixel


0017     unsigned byte   ??              
pixel


........

xxxx     unsigned byte   ??              
pixel

Pixels are organized row-wise. Pixel values are 0 to 255. 0 means background
(white), 255 means foreground (black).

TEST SET LABEL FILE (t10k-labels-idx1-ubyte):

[offset] [type]         
[value]          [description]


0000     32 bit integer  0x00000801(2049)
magic number (MSB first)


0004     32 bit integer  10000           
number of items


0008     unsigned byte   ??              
label


0009     unsigned byte   ??              
label


........

xxxx     unsigned byte   ??              
label

The labels values are 0 to 9.

TEST SET IMAGE FILE (t10k-images-idx3-ubyte):

[offset] [type]         
[value]          [description]


0000     32 bit integer  0x00000803(2051)
magic number


0004     32 bit integer  10000           
number of images


0008     32 bit integer  28              
number of rows


0012     32 bit integer  28              
number of columns


0016     unsigned byte   ??              
pixel


0017     unsigned byte   ??              
pixel


........

xxxx     unsigned byte   ??              
pixel

Pixels are organized row-wise. Pixel values are 0 to 255. 0 means background
(white), 255 means foreground (black).


THE IDX FILE FORMAT

the IDX file format is a simple format for vectors and multidimensional
matrices of various numerical types.

The basic format is

magic number

size in dimension 0

size in dimension 1

size in dimension 2

.....

size in dimension N

data

The magic number is an integer (MSB first). The first 2 bytes are always
0.

The third byte codes the type of the data:

0x08: unsigned byte

0x09: signed byte

0x0B: short (2 bytes)

0x0C: int (4 bytes)

0x0D: float (4 bytes)

0x0E: double (8 bytes)

The 4-th byte codes the number of dimensions of the vector/matrix: 1
for vectors, 2 for matrices....

The sizes in each dimension are 4-byte integers (MSB first, high endian,
like in most non-Intel processors).

The data is stored like in a C array, i.e. the index in the last dimension
changes the fastest.

mnist数据集下载的更多相关文章

  1. mnist数据集下载——mnist数据集提供百度网盘下载地址

    mnist数据集是由深度学习大神 LeCun等人制作完成的数据集,mnist数据集也常认为是深度学习的“ Hello World!”. 官网:http://yann.lecun.com/exdb/mn ...

  2. mnist数据集转换bmp图片

    Mat格式mnist数据集下载地址:http://www.cs.nyu.edu/~roweis/data.html Matlab转换代码: load('mnist_all.mat'); type = ...

  3. tensorflow中使用mnist数据集训练全连接神经网络-学习笔记

    tensorflow中使用mnist数据集训练全连接神经网络 ——学习曹健老师“人工智能实践:tensorflow笔记”的学习笔记, 感谢曹老师 前期准备:mnist数据集下载,并存入data目录: ...

  4. scikit-learn使用fetch_mldata无法下载MNIST数据集的问题

    scikit-learn使用fetch_mldata无法下载MNIST数据集的问题 0. 写在前面 参考书 <Python数据科学手册> 工具 python3.5.1,Jupyter La ...

  5. Caffe初试(二)windows下的cafee训练和测试mnist数据集

    一.mnist数据集 mnist是一个手写数字数据库,由Google实验室的Corinna Cortes和纽约大学柯朗研究院的Yann LeCun等人建立,它有60000个训练样本集和10000个测试 ...

  6. 人工智能大数据,公开的海量数据集下载,ImageNet数据集下载,数据挖掘机器学习数据集下载

    人工智能大数据,公开的海量数据集下载,ImageNet数据集下载,数据挖掘机器学习数据集下载 ImageNet挑战赛中超越人类的计算机视觉系统微软亚洲研究院视觉计算组基于深度卷积神经网络(CNN)的计 ...

  7. 从零到一:caffe-windows(CPU)配置与利用mnist数据集训练第一个caffemodel

    一.前言 本文会详细地阐述caffe-windows的配置教程.由于博主自己也只是个在校学生,目前也写不了太深入的东西,所以准备从最基础的开始一步步来.个人的计划是分成配置和运行官方教程,利用自己的数 ...

  8. 使用libsvm对MNIST数据集进行实验

    使用libsvm对MNIST数据集进行实验 在学SVM中的实验环节,老师介绍了libsvm的使用.当时看完之后感觉简单的说不出话来. 1. libsvm介绍 虽然原理要求很高的数学知识等,但是libs ...

  9. caffe在windows编译project及执行mnist数据集測试

    caffe在windows上的配置和编译能够參考例如以下的博客: http://blog.csdn.net/joshua_1988/article/details/45036993 http://bl ...

随机推荐

  1. WIN10怎么查看端口,并杀死进程

    在命令行执行一下命令 netstat -ano | findstr "

  2. list的泛型

    更新记录 [1]2020.02.12-21:26 1.完善内容 正文 在学习list集合时,我看到书上写list的格式时 List<E> list = new ArrayList<& ...

  3. 深入理解java虚拟机第五部分高效并发

    volatile是java虚拟机提供最轻量级的同步机制. volatile两个特性:1,保证同步的变量对所有线程是可见的.虽然对所有线程是即时可见的,但是却不保证原子性,也就是不保证线程安全,比如对于 ...

  4. Python 中异常嵌套

    在Python中,异常也可以嵌套,当内层代码出现异常时,指定异常类型与实际类型不符时,则向外传,如果与外面的指定类型符合,则异常被处理,直至最外层,运用默认处理方法进行处理,即停止程序,并抛出异常信息 ...

  5. 吴裕雄--天生自然C++语言学习笔记:C++ 数据类型

    使用编程语言进行编程时,需要用到各种变量来存储各种信息.变量保留的是它所存储的值的内存位置.这意味着,当创建一个变量时,就会在内存中保留一些空间. 可能需要存储各种数据类型(比如字符型.宽字符型.整型 ...

  6. MySQL的DDL和DML

    SQL语句:结构化查询语句,使用SQL与数据库“沟通”,完成相应的数据库操作. 语句分类 DDL(Data Definition Languages)语句:即数据库定义语句,用来创建数据库中的表.索引 ...

  7. dp--悬线dp P4147 玉蟾宫

    题目背景 有一天,小猫rainbow和freda来到了湘西张家界的天门山玉蟾宫,玉蟾宫宫主蓝兔盛情地款待了它们,并赐予它们一片土地. 题目描述 这片土地被分成N*M个格子,每个格子里写着'R'或者'F ...

  8. ZJNU 2356 - 六学家

    “选出来三个六学家,他们的编号是i,j,k,满足i<j<k,且a[k]=a[j]-a[i]” 所以输入第i个数a[i]时,直接让答案加上前i-1个数中能构成差值为a[i]的数量即可 然后让 ...

  9. Jetson TX2入门学习之Ubuntu默认密码

    在使用TX2开发板时进行软件更新时需要身份验证,TX2默认有两个登录身份,一个是ubuntu 一个是nvidia 登录其中的哪一个都可以更新   两个身份的密码和登录名是一样的用户:ubuntu 密码 ...

  10. rpm -qa -qc 查询安装过的软件

    dpkg  -l  | grep ssh        #Ubuntu rpm -qa |grep ssh   #centos 通过ps -e |grep ssh命令查看是否启动.如果只有ssh-ag ...