task_categories:image-classification task_ids:multi-class-image-classification size_categories:10K<n<100K source_datasets:extended|cifar10 language:en license:apache-2.0 image classification cifar-10 cifar-10-enriched embeddings enhanced spotlight region:us
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images
per class. There are 50000 training images and 10000 test images.
This version if CIFAR-10 is enriched with several metadata such as embeddings, baseline results and label error scores.
@TECHREPORT{Krizhevsky09learningmultiple,
    author = {Alex Krizhevsky},
    title = {Learning multiple layers of features from tiny images},
    institution = {},
    year = {2009}
}
---
license: apache-2.0
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id: cifar-10
pretty_name: CIFAR-10
size_categories:
- 10K<n<100K
source_datasets:
- extended|cifar10
tags:
- image classification
- cifar-10
- cifar-10-enriched
- embeddings
- enhanced
- spotlight
language:
- en
---
# Dataset Card for CIFAR-10-Enriched (Enhanced by Renumics)

## Dataset Description

- **Homepage:** [Renumics Homepage](https://renumics.com/?hf-dataset-card=cifar10-enriched)
- **GitHub** [Spotlight](https://github.com/Renumics/spotlight)
- **Dataset Homepage** [CS Toronto Homepage](https://www.cs.toronto.edu/~kriz/cifar.html)
- **Paper:** [Learning Multiple Layers of Features from Tiny Images](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf)


### Dataset Summary

📊 [Data-centric AI](https://datacentricai.org) principles have become increasingly important for real-world use cases.  
At [Renumics](https://renumics.com/?hf-dataset-card=cifar10-enriched) we believe that classical benchmark datasets and competitions should be extended to reflect this development. 

🔍 This is why we are publishing benchmark datasets with application-specific enrichments (e.g. embeddings, baseline results, uncertainties, label error scores). We hope this helps the ML community in the following ways:
1. Enable new researchers to quickly develop a profound understanding of the dataset.
2. Popularize data-centric AI principles and tooling in the ML community.
3. Encourage the sharing of meaningful qualitative insights in addition to traditional quantitative metrics.

📚 This dataset is an enriched version of the [CIFAR-10 Dataset](https://www.cs.toronto.edu/~kriz/cifar.html).

### Explore the Dataset

![Analyze CIFAR-10 with Spotlight](https://spotlight.renumics.com/resources/hf-cifar-10-enriched.png)

The enrichments allow you to quickly gain insights into the dataset. The open source data curation tool [Renumics Spotlight](https://github.com/Renumics/spotlight) enables that with just a few lines of code:

Install datasets and Spotlight via [pip](https://packaging.python.org/en/latest/key_projects/#pip):

```python
!pip install renumics-spotlight datasets
```

Load the dataset from huggingface in your notebook:

```python
import datasets

dataset = datasets.load_dataset("renumics/cifar10-enriched", split="train")
```

Start exploring with a simple view:

```python
from renumics import spotlight

df = dataset.to_pandas()
df_show = df.drop(columns=['img'])
spotlight.show(df_show, port=8000, dtype={"img_path": spotlight.Image})
```
You can use the UI to interactively configure the view on the data. Depending on the concrete tasks (e.g. model comparison, debugging, outlier detection) you might want to leverage different enrichments and metadata.

### CIFAR-10 Dataset

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.
The classes are completely mutually exclusive. There is no overlap between automobiles and trucks. "Automobile" includes sedans, SUVs, things of that sort. "Truck" includes only big trucks. Neither includes pickup trucks.

Here is the list of classes in the CIFAR-10:
- airplane										
- automobile										
- bird										
- cat										
- deer										
- dog										
- frog										
- horse										
- ship										
- truck

### 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/image-classification-on-cifar-10).

### Languages

English class labels.

## Dataset Structure

### Data Instances

A sample from the training set is provided below:

```python
{
  'img': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x7FD19FABC1D0>,
  'img_path': '/huggingface/datasets/downloads/extracted/7faec2e0fd4aa3236f838ed9b105fef08d1a6f2a6bdeee5c14051b64619286d5/0/0.png',
  'label': 0,
  'split': 'train'
}
```

### Data Fields

| Feature                         | Data Type                                     |
|---------------------------------|-----------------------------------------------|
| img                             | Image(decode=True, id=None)                   |
| img_path                        | Value(dtype='string', id=None)                |
| label                           | ClassLabel(names=[...], id=None)              |
| split                           | Value(dtype='string', id=None)                |

### Data Splits

| Dataset Split | Number of Images in Split | Samples per Class        |
| ------------- |---------------------------| -------------------------|
| Train         | 50000                     | 5000                     |
| Test          | 10000                     | 1000                     |

## Dataset Creation

### Curation Rationale

The CIFAR-10 and CIFAR-100 are labeled subsets of the [80 million tiny images](http://people.csail.mit.edu/torralba/tinyimages/) dataset. 
They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.

### 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

If you use this dataset, please cite the following paper:
```
@article{krizhevsky2009learning,
  added-at = {2021-01-21T03:01:11.000+0100},
  author = {Krizhevsky, Alex},
  biburl = {https://www.bibsonomy.org/bibtex/2fe5248afe57647d9c85c50a98a12145c/s364315},
  interhash = {cc2d42f2b7ef6a4e76e47d1a50c8cd86},
  intrahash = {fe5248afe57647d9c85c50a98a12145c},
  keywords = {},
  pages = {32--33},
  timestamp = {2021-01-21T03:01:11.000+0100},
  title = {Learning Multiple Layers of Features from Tiny Images},
  url = {https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf},
  year = 2009
}
```

### Contributions

Alex Krizhevsky, Vinod Nair, Geoffrey Hinton, and Renumics GmbH.