cifar100

# Dataset Card for CIFAR-100

## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)

## Dataset Description

- **Homepage:** [CIFAR Datasets](https://www.cs.toronto.edu/~kriz/cifar.html)
- **Repository:**
- **Paper:** [Paper](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf)
- **Leaderboard:**
- **Point of Contact:**

### Dataset Summary

The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images
per class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses.
There are two labels per image - fine label (actual class) and coarse label (superclass).

### Supported Tasks and Leaderboards

- `image-classification`: The goal of this task is to classify a given image into one of 100 classes. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cifar-100).

### Languages

English

## Dataset Structure

### Data Instances

A sample from the training set is provided below:

```
{
'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x2767F58E080>, 'fine_label': 19,
'coarse_label': 11
}
```

### Data Fields

- `img`: A `PIL.Image.Image` object containing the 32x32 image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `fine_label`: an `int` classification label with the following mapping:

`0`: apple

`1`: aquarium_fish

`2`: baby

`3`: bear

`4`: beaver

`5`: bed

`6`: bee

`7`: beetle

`8`: bicycle

`9`: bottle

`10`: bowl

`11`: boy

`12`: bridge

`13`: bus

`14`: butterfly

`15`: camel

`16`: can

`17`: castle

`18`: caterpillar

`19`: cattle

`20`: chair

`21`: chimpanzee

`22`: clock

`23`: cloud

`24`: cockroach

`25`: couch

`26`: cra

`27`: crocodile

`28`: cup

`29`: dinosaur

`30`: dolphin

`31`: elephant

`32`: flatfish

`33`: forest

`34`: fox

`35`: girl

`36`: hamster

`37`: house

`38`: kangaroo

`39`: keyboard

`40`: lamp

`41`: lawn_mower

`42`: leopard

`43`: lion

`44`: lizard

`45`: lobster

`46`: man

`47`: maple_tree

`48`: motorcycle

`49`: mountain

`50`: mouse

`51`: mushroom

`52`: oak_tree

`53`: orange

`54`: orchid

`55`: otter

`56`: palm_tree

`57`: pear

`58`: pickup_truck

`59`: pine_tree

`60`: plain

`61`: plate

`62`: poppy

`63`: porcupine

`64`: possum

`65`: rabbit

`66`: raccoon

`67`: ray

`68`: road

`69`: rocket

`70`: rose

`71`: sea

`72`: seal

`73`: shark

`74`: shrew

`75`: skunk

`76`: skyscraper

`77`: snail

`78`: snake

`79`: spider

`80`: squirrel

`81`: streetcar

`82`: sunflower

`83`: sweet_pepper

`84`: table

`85`: tank

`86`: telephone

`87`: television

`88`: tiger

`89`: tractor

`90`: train

`91`: trout

`92`: tulip

`93`: turtle

`94`: wardrobe

`95`: whale

`96`: willow_tree

`97`: wolf

`98`: woman

`99`: worm

- `coarse_label`: an `int` coarse classification label with following mapping:

`0`: aquatic_mammals

`1`: fish

`2`: flowers

`3`: food_containers

`4`: fruit_and_vegetables

`5`: household_electrical_devices

`6`: household_furniture

`7`: insects

`8`: large_carnivores

`9`: large_man-made_outdoor_things

`10`: large_natural_outdoor_scenes

`11`: large_omnivores_and_herbivores

`12`: medium_mammals

`13`: non-insect_invertebrates

`14`: people

`15`: reptiles

`16`: small_mammals

`17`: trees

`18`: vehicles_1

`19`: vehicles_2

### Data Splits

| name |train|test|
|----------|----:|---------:|
|cifar100|50000| 10000|

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

```
@TECHREPORT{Krizhevsky09learningmultiple,
author = {Alex Krizhevsky},
title = {Learning multiple layers of features from tiny images},
institution = {},
year = {2009}
}
```

### Contributions

Thanks to [@gchhablani](https://github.com/gchablani) for adding this dataset.

# CIFAR-100 数据集卡片

## 目录
- [数据集描述](#dataset-description)
- [数据集概述](#dataset-summary)
- [支持任务与排行榜](#supported-tasks-and-leaderboards)
- [语言](#languages)
- [数据集结构](#dataset-structure)
- [数据实例](#data-instances)
- [数据字段](#data-fields)
- [数据划分](#data-splits)
- [数据集构建](#dataset-creation)
- [构建初衷](#curation-rationale)
- [源数据](#source-data)
- [标注信息](#annotations)
- [个人与敏感信息](#personal-and-sensitive-information)
- [数据集使用注意事项](#considerations-for-using-the-data)
- [数据集社会影响](#social-impact-of-dataset)
- [偏差讨论](#discussion-of-biases)
- [其他已知局限性](#other-known-limitations)
- [附加信息](#additional-information)
- [数据集整理者](#dataset-curators)
- [授权信息](#licensing-information)
- [引用信息](#citation-information)
- [贡献](#contributions)

## 数据集描述

- **主页：** [CIFAR 数据集](https://www.cs.toronto.edu/~kriz/cifar.html)
- **代码仓库：**
- **论文：** [论文链接](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf)
- **排行榜：**
- **联系方式：**

### 数据集概述
CIFAR-100 数据集包含100个类别下的60000张32×32彩色图像，每个类别包含600张图像。每个类别配有500张训练图像与100张测试图像，整体训练集共计50000张图像，测试集共计10000张图像。该100个类别被划分为20个超类。每张图像均带有两个标签——细标签（fine label，对应实际类别）与粗标签（coarse label，对应超类）。

### 支持任务与排行榜
- `图像分类（image-classification）`：该任务的目标是将给定图像分类至100个类别之一。排行榜可参见[此处](https://paperswithcode.com/sota/image-classification-on-cifar-100)。

### 语言
英语

## 数据集结构

### 数据实例
以下展示训练集的一个样本：

{
'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x2767F58E080>, 'fine_label': 19,
'coarse_label': 11
}

### 数据字段
- `img`：包含32×32图像的PIL图像对象（PIL.Image.Image）。请注意，当访问图像列时：`dataset[0]["image"]` 会自动对图像文件进行解码。解码大量图像文件可能会耗费大量时间，因此建议始终先查询样本索引再访问`"image"`列，即优先使用`dataset[0]["image"]`而非`dataset["image"][0]`。
- `fine_label`：整数类型的分类标签，对应映射关系如下：

0: 苹果
1: 水族箱鱼类
2: 婴儿
3: 熊
4: 海狸
5: 床
6: 蜜蜂
7: 甲虫
8: 自行车
9: 瓶子
10: 碗
11: 男孩
12: 桥梁
13: 公共汽车
14: 蝴蝶
15: 骆驼
16: 罐头
17: 城堡
18: 毛虫
19: 牛
20: 椅子
21: 黑猩猩
22: 时钟
23: 云朵
24: 蟑螂
25: 长沙发
26: 螃蟹
27: 鳄鱼
28: 杯子
29: 恐龙
30: 海豚
31: 大象
32: 比目鱼
33: 森林
34: 狐狸
35: 女孩
36: 仓鼠
37: 房屋
38: 袋鼠
39: 键盘
40: 台灯
41: 割草机
42: 豹
43: 狮子
44: 蜥蜴
45: 龙虾
46: 男人
47: 枫树
48: 摩托车
49: 山脉
50: 老鼠
51: 蘑菇
52: 橡树
53: 橙子
54: 兰花
55: 水獭
56: 棕榈树
57: 梨
58: 皮卡卡车
59: 松树
60: 平原
61: 盘子
62: 罂粟花
63: 豪猪
64: 负鼠
65: 兔子
66: 浣熊
67: 鳐鱼
68: 道路
69: 火箭
70: 玫瑰
71: 海洋
72: 海豹
73: 鲨鱼
74: 鼩鼱
75: 臭鼬
76: 摩天大楼
77: 蜗牛
78: 蛇
79: 蜘蛛
80: 松鼠
81: 有轨电车
82: 向日葵
83: 甜椒
84: 桌子
85: 坦克
86: 电话
87: 电视机
88: 老虎
89: 拖拉机
90: 火车
91: 鳟鱼
92: 郁金香
93: 乌龟
94: 衣柜
95: 鲸鱼
96: 柳树
97: 狼
98: 女人
99: 蠕虫

- `coarse_label`：整数类型的粗分类标签，对应映射关系如下：

0: 水生哺乳动物
1: 鱼类
2: 花卉
3: 食品容器
4: 水果与蔬菜
5: 家用电气设备
6: 家用家具
7: 昆虫
8: 大型食肉动物
9: 大型人工户外设施
10: 大型自然户外场景
11: 大型杂食与植食动物
12: 中型哺乳动物
13: 非昆虫无脊椎动物
14: 人物
15: 爬行动物
16: 小型哺乳动物
17: 树木
18: 交通工具1类
19: 交通工具2类

### 数据划分
| 数据集名称 | 训练集样本数 | 测试集样本数 |
| :-------- | ----------: | -----------: |
| cifar100 | 50000 | 10000 |

## 数据集构建

### 构建初衷
[需补充更多信息]

### 源数据

#### 初始数据收集与归一化
[需补充更多信息]

#### 源语言创作者是谁？
[需补充更多信息]

### 标注信息

#### 标注流程
[需补充更多信息]

#### 标注者是谁？
[需补充更多信息]

### 个人与敏感信息
[需补充更多信息]

## 数据集使用注意事项

### 数据集社会影响
[需补充更多信息]

### 偏差讨论
[需补充更多信息]

### 其他已知局限性
[需补充更多信息]

## 附加信息

### 数据集整理者
[需补充更多信息]

### 授权信息
[需补充更多信息]

### 引用信息

@TECHREPORT{Krizhevsky09learningmultiple,
author = {Alex Krizhevsky},
title = {Learning multiple layers of features from tiny images},
institution = {},
year = {2009}
}

### 贡献
感谢 [@gchhablani](https://github.com/gchablani) 贡献本数据集。