README.md

---
license: cc
task_categories:
- text-to-image
tags:
- art
size_categories:
- 10M<n<100M
---
# Dataset Card for Fondant Creative Commons 25 million (fondant-cc-25m)



![image/png](https://cdn-uploads.huggingface.co/production/uploads/6266919100f1a3335dbd966f/kNWHIfjZWDG_IqN4lQYTm.png)

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Changelog](#changelog)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [How to use it](#how-to-use-it)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [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)
- [Terms of Use for Dataset](#terms-of-use-for-the-dataset)


## Dataset Description
- **Homepage:** https://www.fondant.ai/
- **Repository:** https://github.com/ml6team/fondant
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** info@fondant.ai

## Changelog
|Release|Description|
|-|-|
|v0.1| Release of the Fondant-cc-25m dataset

## Dataset Summary
Fondant-cc-25m contains 25 million image URLs available under different [Creative Commons](https://creativecommons.org/) 
licenses collected from the [Common Crawl web corpus](https://commoncrawl.org/). 
The dataset was created using [Fondant](https://fondant.ai), an open-source framework for building composable data 
preparation pipelines for training (and fine-tuning) foundation models.

## Supported Tasks and Leaderboards

This dataset can be used for training or fine-tuning image generation or computer vision models.

## How to use it

We have prepared a sample fondant pipeline that limits the dataset to PNG files and downloads the images.

To execute the pipeline locally, you must have [docker compose](https://docs.docker.com/compose/),
[Python](https://python.org) >=3.8 and [Git](https://git-scm.com/) installed on your system.

First install Fondant by running:

```bash
pip install fondant
```

Then clone the [Fondant GitHub repository](https://github.com/ml6team/fondant)

```bash
git clone https://github.com/ml6team/fondant.git
```

Then make sure that Docker Compose is running, navigate to `examples/pipelines/filter-cc-25m`, and initiate the pipeline by executing:

```bash
fondant run pipeline --local
```

To visually inspect the results quickly, you can use:

```bash
fondant explore --base_path ./data
```

## Dataset Structure
### Data Instances
Each data instance corresponds to one image. The URL of the image is in the `image_url` feature, and other features (`alt_text`, `webpage_url`, etc) provide some 
metadata. Note that images have been deduplicated only based on their URLs.

### Data Fields
- `image_url` (string): image url to download the image
- `alt_text` (string): alternative text of the image
- `webpage_url` (string): webpage source of the image
- `license_type` (string): creative commons license type of the image
- `license_location` (string): location of the license on the webpage
- `sort_url` (string): sort friendly image url with top level domain as the prefix

## Data Splits
The dataset has no splits and all data is loaded as a [TODO: check split name - train?] split by default. If you want to setup a custom train-test split 
beware that the dataset may contain duplicates which can cause leakage into the test split.


## Dataset Creation
### Curation Rationale
Current AI image generation models such as Stable Diffusion and Dall-E trained on hundreds of millions of images from the public Internet tend to include copyrighted work. 
This creates legal risks and uncertainties for users of image generation systems, and is unfair towards copyright holders who may not want their proprietary work reproduced without consent. 
By releasing an open large-scale image dataset, we hope to mitigate legal risks and empower ethical AI development that is respectful of copyrighted content.
This dataset is the first step towards our final goal of a 500M Creative Commons image dataset.

### Source Data
#### Initial Data Collection and Normalization
##### License detection 
Permissive licenses have minimal restrictions on how the image can be copied, modified, and redistributed. 
The full list of licenses can be found [here](https://creativecommons.org/about/cclicenses/).

We examined HTML tags of the webpages for the presence of Creative Commons license URLs. A webpage was marked permissive if only a license URL was found in its footer, aside or sidebar. Subsequently, all the image URLs present on the web page were collected together with the license information.
This approach achieved a 96.32% accuracy in an earlier test where we investigated the scope of licenses. More information on our approach can be found in [this blogpost](https://blog.ml6.eu/ai-image-generation-without-copyright-infringement-a9901b64541c).


#### Who are the source language producers?
The source language producers are users of the Internet whose webpages were included in the Common Crawl indices `CC-MAIN-2023-23`, `CC-MAIN-2023-06` and `CC-MAIN-2023-14` spanning January to April 2023.

### Personal and Sensitive Information
The released dataset may contain sensitive information such as names, emails and addresses that have previously been published to the Internet. In the event that the dataset contains personal information, researchers should only use public, non-personal information in support of conducting and publishing their [open-access](https://en.wikipedia.org/wiki/Open_access) research. Personal information should not be used for spamming purposes, including sending unsolicited emails or selling of personal information. Complaints, removal requests, and "do not contact" requests can be sent to info@fondant.ai.

The PII filtering pipeline for this dataset is still a work in progress. Researchers that wish to contribute to the anonymization pipeline of the project can join [here](https://github.com/ml6team/fondant/tree/main#-contributing). 


### Opting out of Fondant-cc-25m
We are giving the public the ability to have their image removed from the dataset upon request. The process for submitting and enacting removal requests will keep evolving throughout the project as we receive feedback and build up more data governance tools.
If you'd like to have your data removed from the dataset, [contact us](mailto:info@fondant.ai).

## Considerations for Using the Data
### Discussion of Biases
### Other Known Limitations

## Additional Information

### Dataset Curators
1. Sharon Grundmann, ML6, sharon.grundmann@ml6.eu
2. Robbe Sneyders, ML6, robbe.sneyders@ml6.eu
3. Matthias Richter, ML6, matthias.richter@ml6.eu


### Licensing Information
Fondant-cc-25m is a collection of images with various Creative Commons and other public licenses. Any use of all or part of the images gathered in Fondant-cc-25m must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point.

The list of Creative Commons license types included in the dataset can be found [here](https://creativecommons.org/about/cclicenses/).

### Citation Information
```
[TODO]
```

### Contributions

[More Information Needed]

## Terms of Use for the [Dataset]