--- annotations_creators: - found language_creators: - found language: - en license: - other multilinguality: - monolingual size_categories: - 10M 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 ```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.