Datasets:
annotations_creators:
- found
language_creators:
- found
languages:
- en
licenses:
- 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 Card for Conceptual 12M
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Repository: Conceptual 12M repository
- Paper: Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
- Point of Contact: Conceptual Captions e-mail
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:
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 . 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 . 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
@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 for adding this dataset.