Datasets:
conceptual_12m

	---
	annotations_creators:
	- found
	language_creators:
	- found
	language:
	- en
	license:
	- other
	multilinguality:
	- monolingual
	size_categories:
	- 10M<n<100M
	source_datasets:
	- original
	task_categories:
	- image-to-text
	task_ids:
	- image-captioning
	paperswithcode_id: cc12m
	pretty_name: Conceptual 12M
	dataset_info:
	features:
	- name: image_url
	dtype: string
	- name: caption
	dtype: string
	splits:
	- name: train
	num_bytes: 2794168030
	num_examples: 12423374
	download_size: 2707204412
	dataset_size: 2794168030
	---

	# Dataset Card for Conceptual 12M

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Dataset Preprocessing](#dataset-preprocessing)
	- [Supported Tasks](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-instances)
	- [Data Splits](#data-instances)
	- [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)

	## Dataset Description

	- Repository: [Conceptual 12M repository](https://github.com/google-research-datasets/conceptual-12m)
	- Paper: [Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts](https://arxiv.org/abs/2102.08981)
	- Point of Contact: [Conceptual Captions e-mail](mailto:conceptual-captions@google.com)

	### Dataset Summary

	Conceptual 12M (CC12M) is a dataset with 12 million image-text pairs specifically meant to be used for visionand-language pre-training.
	Its data collection pipeline is a relaxed version of the one used in Conceptual Captions 3M (CC3M).

	### Dataset Preprocessing

	This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:

	```python
	from concurrent.futures import ThreadPoolExecutor
	from functools import partial
	import io
	import urllib

	import PIL.Image

	from datasets import load_dataset
	from datasets.utils.file_utils import get_datasets_user_agent


	USER_AGENT = get_datasets_user_agent()


	def fetch_single_image(image_url, timeout=None, retries=0):
	for _ in range(retries + 1):
	try:
	request = urllib.request.Request(
	image_url,
	data=None,
	headers={"user-agent": USER_AGENT},
	)
	with urllib.request.urlopen(request, timeout=timeout) as req:
	image = PIL.Image.open(io.BytesIO(req.read()))
	break
	except Exception:
	image = None
	return image


	def fetch_images(batch, num_threads, timeout=None, retries=0):
	fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
	with ThreadPoolExecutor(max_workers=num_threads) as executor:
	batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"]))
	return batch


	num_threads = 20
	dset = load_dataset("conceptual_12m")
	dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
	```

	### Supported Tasks and Leaderboards

	- `image-captioning`: This dataset can be used to train model for the Image Captioning task.

	### Languages

	All captions are in English.

	## Dataset Structure

	### Data Instances

	Each instance represents a single image with a caption:

	```
	{
	'image_url': 'http://lh6.ggpht.com/-IvRtNLNcG8o/TpFyrudaT6I/AAAAAAAAM6o/_11MuAAKalQ/IMG_3422.JPG?imgmax=800',
	'caption': 'a very typical bus station'
	}
	```

	### Data Fields

	- `image_url`: Static URL for downloading the image associated with the post.
	- `caption`: Textual description of the image.

	### Data Splits

	There is only training data, with a total of 12423374 rows

	## Dataset Creation

	### Curation Rationale

	Conceptual 12M shares the same pipeline with Conceptual Captions (CC3M), but relaxes some processing steps.

	### Source Data

	#### Initial Data Collection and Normalization

	From the paper:
	> To arrive at CC12M, we keep
	the image-text filtering intact, and relax the unimodal filters only. First, for image-based filtering, we set the maximum ratio of larger to smaller dimension to 2.5 instead of 2.
	We still keep only JPEG images with size greater than
	400 pixels, and still exclude images that trigger pornography detectors. Second, in text-based filtering, we allow text
	between 3 and 256 words in the alt-text. We still discard
	candidates with no noun or no determiner, but permit ones
	without prepositions. We discard the heuristics regarding
	high unique-word ratio covering various POS tags and word
	capitalization. We set the maximum fraction of word repetition allowed to 0.2. Given a larger pool of text due to the
	above relaxations, the threshold for counting a word type as
	rare is increased from 5 to 20

	> The main motivation for CC3M to
	perform text transformation is that a majority of candidate
	captions contain ultrafine-grained entities such as proper
	names (people, venues, locations, etc.), making it extremely
	difficult to learn as part of the image captioning task. In
	contrast, we are not restricted by the end task of image caption generation. Our intuition is that relatively more difficult pre-training data would lead to better transferability.
	We thus do not perform hypernimization or digit substitution. [...] The only exception to the “keep alt-texts as
	raw as possible” rule is performing person-name substitutions, which we identify as necessary to protect the privacy
	of the individuals in these images. For this step, we use the
	Google Cloud Natural Language APIs to detect all named
	entities of type Person, and substitute them by a special token <PERSON>. Around 25% of all the alt-texts in CC12M
	are transformed in this fashion.

	#### Who are the source language producers?

	Not specified.

	### Annotations

	#### Annotation process

	Annotations are extracted jointly with the images using the automatic pipeline.

	#### Who are the annotators?

	Not specified.

	### Personal and Sensitive Information

	From the paper:

	> The only exception to the “keep alt-texts as
	raw as possible” rule is performing person-name substitutions, which we identify as necessary to protect the privacy
	of the individuals in these images. For this step, we use the
	Google Cloud Natural Language APIs to detect all named
	entities of type Person, and substitute them by a special token <PERSON>. Around 25% of all the alt-texts in CC12M
	are transformed in this fashion.

	## 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

	Soravit Changpinyo, Piyush Sharma, Nan Ding and Radu Soricut.

	### Licensing Information

	The dataset may be freely used for any purpose, although acknowledgement of
	Google LLC ("Google") as the data source would be appreciated. The dataset is
	provided "AS IS" without any warranty, express or implied. Google disclaims all
	liability for any damages, direct or indirect, resulting from the use of the
	dataset.

	### Citation Information

	```bibtex
	@inproceedings{changpinyo2021cc12m,
	title = {{Conceptual 12M}: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts},
	author = {Changpinyo, Soravit and Sharma, Piyush and Ding, Nan and Soricut, Radu},
	booktitle = {CVPR},
	year = {2021},
	}
	```

	### Contributions

	Thanks to [@thomasw21](https://github.com/thomasw21) for adding this dataset.