简 介: 本文总结了部分MATLAB中用于深度学习的数据集合。
关键词 : MATLAB,DEEPLENARING
MATLAB数据
文章目录
合成数字图片
MNSIT手写数字图片
字母表
FLower数据集合
食物图片
Cifar-10
零售商品图片集合
街景数据
车辆Vechicle
RIT-18纽约地
区无人机图片
BraTS脑肿瘤
核磁共振图片
数据库名称与数量
Camelyon16
Challenge
数据集合
TC-12
RGB
See-in-The-Dark
Wild
Classification
总 结
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§01 MATLAB数据
??在 Data Sets for Deep Learning 给出了MATLAB中用于深度学习的数据集合介绍以及下载方法。
1.1 合成数字图片
??这是一个10000个灰度合成数字姿态的数字集合。类似于MNIST,但它是合成的。
??问题来了,这些数字是如何被合成的?在哪儿可以下载到原始的数据集合呢?
数据库参数:
-
数量 :10000
尺寸 :28×28
色彩 :灰度图片
▲ 图1.1.1 MATLAB Digits Dataset
1.2 MNSIT手写数字图片
??该集合包括有70,000个图片,分为60,000训练集合以及10,000个测试集合。
图片库参数:
-
数量 :70,000
色彩 :灰度图片
尺寸 :28×28
▲ 图1.2.1 MNIST代表数字
1.3 字母表
??Omniglot数据集合包含有50个字母表,保安有30个训练集合,20个测试集合。 每个字符包含有一定数量EZif是, Ojibwe编号:14(这是加拿大欧土著音节字符), Tifinagh:编号55。每个字符有20个手写字体。
1.4 FLower数据集合
??这是一个3670个花朵图片数据集合,分为五大类:Daisy(黛西), Dandelion(蒲公英), Roses(玫瑰花), Sunflowers(向日葵), Tulips(郁金香)。
数据库参数:
-
数量 :3670
色彩 :彩色
种类 :5类
文件大小 :218MB
1.5 食物图片
图片库参数:
-
数量 :978
色彩 :彩色
种类 :9类:Caesar_Salad, Caprese_salard, French_fires, Greek_salard, Hamburger, Hot_dog, Pizza, Sashimi, Suhi.
数据文件 :77MB
▲ 图1.5.1 食物图片
1.6 Cifar-10
数据库参数:
-
数量 :60,000
色彩 :彩色
尺寸 :32×32
种类 :10个类别:Airplane,Automobile,Bird,Car,Deer,Dog,Frog,Horse,Ship,Truck
每个类别 :6000
▲ 图1.6.1 Cifar10图片
1.7 零售商品图片集合
??这个数据集合包括有5类Mathworks公司相关的零售商品。
数据集合参数:
-
数量 :不详
种类 :5类:Cap, Cube, Playing Cards, Torch
尺寸 :227×227
色彩 :彩色
▲ 图1.7.1 Mathworks 零售商品图片集
1.8 街景数据
??CamVid 数据集合是一组街景图品集合,从小轿车内部拍摄。用于训练网络对图片进行语义分割。改数据集合提供了32类像素级别语义标注。包括:轿车,行人,道路等。
数据参数:
-
数量 :不详
尺寸 :720×960
色彩 :彩色
文件大小 :573MB
▲ 图1.8.1 CamVid 街景图片数据集合
1.9 车辆Vechicle
??Vehicle数据集合包括有295个图片,其中包含有1到2个车龄。适合于YOLO-v2的图像定位训练,但如果要达到实际应用,还需要更多的标注图片。
数据集合参数:
-
数量 :295
色彩 :彩色
尺寸 :720×960
1.10 RIT-18纽约地区无人机图片
??这个数据集合包括有四旋翼无人机在纽约 Hamlin Beach 州立公园拍摄的图片。包括有18种物品标注:道路标志,树木,建筑物。
数据库参数:
-
文件大小 :3GB
色彩 :彩色
种类 :18种类
▲ 图1.10.1 RIT-18数据集合
1.11 BraTS脑肿瘤核磁共振图片
??BarTS数据集合包含有脑肿瘤(神经胶质瘤 Glioms)这是主要脑部病变。
数据库参数:
-
数量 :740
维度 :4D
尺寸 :240×240×155×4
文件大小 :7GB
▲ 图1.11.1 脑部肿瘤数据库
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§02 数据库名称与数量
2.1 Camelyon16
▲ 图2.1.1 Camelyon16
2.2 Challenge
▲ 图2.2.1 Low Dose CTGrand Challenge
2.3 数据集合
▲ 图2.3.1 COCO:Common Objects in Context
2.4 TC-12
▲ 图2.4.1 IAPRTC-12
2.5 RGB
▲ 图2.5.1 Zuirch RAW to RGB
2.6 See-in-The-Dark
▲ 图2.6.1 See-In-The-Dark
2.7 Wild
▲ 图2.7.1 LIVE in the Wild
2.8 Classification
▲ 图2.8.1 Conrete Crake Image for Classifiction
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※ 总??结 ※
??本文总结了部分MATLAB中用于深度学习的数据集合。
■ 相关文献链接:
● 相关图表链接:
◎ 参考文档:
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