 
# Dataset Card for CIFAR-10-LT (Long Tail)

## 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)
- [Additional Information](#additional-information)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [CIFAR Datasets](https://www.cs.toronto.edu/~kriz/cifar.html)
- **Paper:** [Paper imbalanced example](https://openaccess.thecvf.com/content_CVPR_2019/papers/Cui_Class-Balanced_Loss_Based_on_Effective_Number_of_Samples_CVPR_2019_paper.pdf)
- **Leaderboard:** [r-10](https://paperswithcode.com/sota/long-tail-learning-on-cifar-10-lt-r-10) [r-100](https://paperswithcode.com/sota/long-tail-learning-on-cifar-10-lt-r-100)
 
### Dataset Summary
 
The CIFAR-10-LT imbalanced dataset is comprised of under 60,000 color images, each measuring 32x32 pixels, 
distributed across 10 distinct classes. 
The number of samples within each class decreases exponentially with factors of 10 and 100. 
The dataset includes 10,000 test images, with 1000 images per class, 
and fewer than 50,000 training images. 
Each image is assigned one label.

### Supported Tasks and Leaderboards

- `image-classification`: The goal of this task is to classify a given image into one of 10 classes. The leaderboard is available [here](https://paperswithcode.com/sota/long-tail-learning-on-cifar-10-lt-r-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>, 'label': 0
}
```

### 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]`
- label: 0-9 with the following correspondence
         0 airplane
         1 automobile
         2 bird
         3 cat
         4 deer
         5 dog
         6 frog
         7 horse
         8 ship
         9 truck

 
### Data Splits
 
|   name   |train|test|
|----------|----:|---------:|
|cifar10|<50000|     10000|
 
### Licensing Information
Apache License 2.0
 
### 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) and all contributors for adding the original balanced cifar10 dataset.
task_categories:image-classification annotations_creators:crowdsourced language_creators:found multilinguality:monolingual size_categories:10K<n<100K source_datasets:cifar10 language:en license:apache-2.0 region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": "apache-2.0", "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["cifar10"], "task_categories": ["image-classification"], "task_ids": [], "paperswithcode_id": "cifar-10", "pretty_name": "Cifar10-LT", "dataset_info": {"features": [{"name": "img", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "airplane", "1": "automobile", "2": "bird", "3": "cat", "4": "deer", "5": "dog", "6": "frog", "7": "horse", "8": "ship", "9": "truck"}}}}], "config_name": "cifar10", "splits": [{"name": "train"}, {"name": "test", "num_bytes": 22772838, "num_examples": 10000}], "download_size": 170498071}}
TAGS
#task_categories-image-classification #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-cifar10 #language-English #license-apache-2.0 #region-us 

Dataset Card for CIFAR-10-LT (Long Tail)
========================================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Additional Information
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage: CIFAR Datasets
* Paper: Paper imbalanced example
* Leaderboard: r-10 r-100


### Dataset Summary


The CIFAR-10-LT imbalanced dataset is comprised of under 60,000 color images, each measuring 32x32 pixels,
distributed across 10 distinct classes.
The number of samples within each class decreases exponentially with factors of 10 and 100.
The dataset includes 10,000 test images, with 1000 images per class,
and fewer than 50,000 training images.
Each image is assigned one label.


### Supported Tasks and Leaderboards


* 'image-classification': The goal of this task is to classify a given image into one of 10 classes. The leaderboard is available here.


### Languages


English


Dataset Structure
-----------------


### Data Instances


A sample from the training set is provided below:


### 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]'
* label: 0-9 with the following correspondence
0 airplane
1 automobile
2 bird
3 cat
4 deer
5 dog
6 frog
7 horse
8 ship
9 truck


### Data Splits



### Licensing Information


Apache License 2.0


### Contributions


Thanks to @gchhablani and all contributors for adding the original balanced cifar10 dataset.



### Dataset Summary


The CIFAR-10-LT imbalanced dataset is comprised of under 60,000 color images, each measuring 32x32 pixels,
distributed across 10 distinct classes.
The number of samples within each class decreases exponentially with factors of 10 and 100.
The dataset includes 10,000 test images, with 1000 images per class,
and fewer than 50,000 training images.
Each image is assigned one label. ### Supported Tasks and Leaderboards


* 'image-classification': The goal of this task is to classify a given image into one of 10 classes. The leaderboard is available here. ### Languages


English


Dataset Structure
----------------- ### Data Instances


A sample from the training set is provided below: ### 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]'
* label: 0-9 with the following correspondence
0 airplane
1 automobile
2 bird
3 cat
4 deer
5 dog
6 frog
7 horse
8 ship
9 truck ### Data Splits ### Licensing Information


Apache License 2.0 ### Contributions


Thanks to @gchhablani and all contributors for adding the original balanced cifar10 dataset.
TAGS
#task_categories-image-classification #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-cifar10 #language-English #license-apache-2.0 #region-us 
 ### Dataset Summary


The CIFAR-10-LT imbalanced dataset is comprised of under 60,000 color images, each measuring 32x32 pixels,
distributed across 10 distinct classes.
The number of samples within each class decreases exponentially with factors of 10 and 100.
The dataset includes 10,000 test images, with 1000 images per class,
and fewer than 50,000 training images.
Each image is assigned one label. ### Supported Tasks and Leaderboards


* 'image-classification': The goal of this task is to classify a given image into one of 10 classes. The leaderboard is available here. ### Languages


English


Dataset Structure
----------------- ### Data Instances


A sample from the training set is provided below: ### 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]'
* label: 0-9 with the following correspondence
0 airplane
1 automobile
2 bird
3 cat
4 deer
5 dog
6 frog
7 horse
8 ship
9 truck ### Data Splits ### Licensing Information


Apache License 2.0 ### Contributions


Thanks to @gchhablani and all contributors for adding the original balanced cifar10 dataset.