Datasets:
Tasks:
Image-to-Text
Sub-tasks:
image-captioning
Languages:
English
Size:
10M<n<100M
ArXiv:
License:
| annotations_creators: | |
| - found | |
| language_creators: | |
| - found | |
| languages: | |
| - en | |
| licenses: | |
| - cc-by-4.0 | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 10M<n<100M | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - image-to-text | |
| task_ids: | |
| - image-captioning | |
| paperswithcode_id: pmd | |
| pretty_name: PMD | |
| extra_gated_prompt: | | |
| By clicking on “Access repository” below, you also agree to individual licensing terms for each of the subset datasets of the PMD as noted at https://huggingface.co/datasets/facebook/pmd#additional-information. | |
| # Dataset Card for PMD | |
| ## Table of Contents | |
| - [Table of Contents](#table-of-contents) | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Dataset Preprocessing](#dataset-preprocessing) | |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [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) | |
| - [Contributions](#contributions) | |
| ## Dataset Description | |
| - **Homepage:** [PMD homepage](https://flava-model.github.io/) | |
| - **Repository:** [PMD repository](https://huggingface.co/datasets/facebook/pmd) | |
| - **Paper:** [FLAVA: A Foundational Language And Vision Alignment Model | |
| ](https://arxiv.org/abs/2112.04482) | |
| - **Leaderboard:** | |
| - **Point of Contact:** [Amanpreet Singh](mailto:[email protected]) | |
| ### Dataset Summary | |
| Introduced in the FLAVA paper, Public Multimodal Dataset (PMD) is a collection of publicly-available image-text pair datasets. PMD contains 70M image-text pairs in total with 68M unique images. The dataset contains pairs from Conceptual Captions, Conceptual Captions 12M, WIT, Localized Narratives, RedCaps, COCO, SBU Captions, Visual Genome and a subset of YFCC100M dataset. | |
| If you use PMD, please cite the original FLAVA paper as follows, along with the individual datasets (!! - see below for references): | |
| ```bibtex | |
| @inproceedings{singh2022flava, | |
| title={Flava: A foundational language and vision alignment model}, | |
| author={Singh, Amanpreet and Hu, Ronghang and Goswami, Vedanuj and Couairon, Guillaume and Galuba, Wojciech and Rohrbach, Marcus and Kiela, Douwe}, | |
| booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, | |
| pages={15638--15650}, | |
| year={2022} | |
| } | |
| ``` | |
| You can load this dataset by first logging into Hugging Face using `huggingface-cli login` and then running the following commands: | |
| ```py | |
| from datasets import load_dataset | |
| pmd = load_dataset("facebook/pmd", use_auth_token=True) | |
| ``` | |
| You can also load the dataset in streaming mode if you don't want to download the big dataset files locally: | |
| ```py | |
| pmd = load_dataset("facebook/pmd", use_auth_token=True, streaming=True) | |
| ``` | |
| ### Dataset Preprocessing | |
| This dataset doesn't download all of the images locally by default. Instead, it exposes URLs for some of 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_data, timeout=None, retries=0): | |
| image_url, image = image_data | |
| if image is not None: | |
| return image | |
| 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, zip(batch["image_url"], batch["image"]))) | |
| return batch | |
| num_threads = 20 | |
| dset = load_dataset("pmd") | |
| dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads}) | |
| ``` | |
| You can also save the dataset to disk for faster and direct loading next time but beware of the space required: `dset.save_to_disk(/path/to/save)`. | |
| ### Supported Tasks and Leaderboards | |
| In the FLAVA paper, the dataset has been used to pretrain the FLAVA model as a source of well-aligned image-text pairs. This allows having a generic vision-and-language model which can be fine-tuned for a variety of tasks. | |
| We anticipate that the dataset can be used to train deep neural networks that perform image captioning and that learn transferable visual representations for a variety of downstream visual recognition tasks (image classification, object detection, instance segmentation). We also anticipate that the dataset could be used for a variety of vision-and-language (V&L) tasks, such as image or text retrieval or text-to-image synthesis. | |
| ### Languages | |
| All of the subsets in PMD use English as their primary language. | |
| ## Dataset Structure | |
| ### Data Instances | |
| Each instance in PMD represents a single image-text pair: | |
| ``` | |
| { | |
| 'image_url': None, | |
| 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7FCFF86A1E80>, | |
| 'texts': ['A woman wearing a net on her head cutting a cake. '], | |
| 'source': 'coco', | |
| 'meta': '{\n "annotation": [\n "A woman wearing a net on her head cutting a cake. "\n ],\n "image_path": "zip:/val2014/COCO_val2014_000000522418.jpg::http:/images.cocodataset.org/zips/val2014.zip"\n}' | |
| } | |
| ``` | |
| ### Data Fields | |
| - `image_url`: Static URL for downloading the image associated with the text. Can be `None` if image is locally available. | |
| - `image`: A PIL Image object for the image associated with the text. Can be `None` if image is not locally available. | |
| - `texts`: `List<str>`, The textual descriptions of the image. | |
| - `source`: `str`, The PMD subset which this pair is from. | |
| - `meta`: `str`, A json representation of the original annotation from the dataset. | |
| ### Data Splits | |
| All the data is contained in the training set. The training set has nearly 70M instances. | |
| We intend for this dataset to be primarily used for pre-training with one or more specific downstream task(s) in mind. Thus, all of the instances should be used for pretraining. If required, we specifically make sure that there is no overlap with Karpathy's COCO validation set so users can use that subset for any validation purposes. Users can also load Karpathy's val subset by specifying the "validation" split while loading PMD. This will also load other "validation" splits for some subsets, if they are available. | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| From the paper: | |
| > Purely contrastive methods, however, also have important shortcomings. Their cross-modal nature does not make them easily usable on multimodal problems that require dealing with both modalities at the same time. They require large corpora, which for both CLIP and ALIGN have not been made accessible to the research community and the details of which remain shrouded in mystery, notwithstanding well-known issues with the construction of such datasets | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| From the paper: | |
| > **Data Collection Pipeline** | |
| For the YFCC100M dataset, we filter the image-text data by discarding non-English captions and only keeping captions that contain more than two words. description field of each image, if this does not pass our filters we consider the title field. Other than that, we did not do any additional filtering. | |
| #### Who are the source language producers? | |
| Please refer to the original dataset papers to understand where the content is coming from. | |
| ### Annotations | |
| #### Annotation process | |
| The dataset is a combination of existing public datasets with some filtering applied on top so there is no annotation process involved. | |
| #### Who are the annotators? | |
| Please refer to the original dataset papers to understand where the content is coming from. | |
| ### Personal and Sensitive Information | |
| Please refer to the original dataset papers to understand where the content is coming from. For example, a detailed description on this for RedCaps can be found [here](https://huggingface.co/datasets/red_caps). | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| From the paper: | |
| > **Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., | |
| a data protection impact analysis) been conducted?** | |
| No. | |
| ### Discussion of Biases | |
| Please refer to the original dataset papers to understand where the content is coming from. For example, a detailed description on this for RedCaps can be found [here](https://huggingface.co/datasets/red_caps). | |
| ### Other Known Limitations | |
| From the paper: | |
| > **Are there any errors, sources of noise, or redundancies in the dataset?** | |
| PMD is noisy by design since image-text pairs on the internet are noisy and unstructured. Though, since it contains sources such as COCO, Visual Genome, and Localized Narratives which are hand-curated by annotators, it has a lot of well-aligned data as well. So, it is definitely more aligned compared to e.g. LAION. | |
| Some instances may also have duplicate images and captions but should have almost no effect in training large-scale models. | |
| > **Does the dataset contain data that might be considered confidential (e.g., data that is | |
| protected by legal privilege or by doctor-patient confidentiality, data that includes the | |
| content of individuals non-public communications)?** | |
| Not that the authors know of. Please refer to the original dataset papers to understand where the content is coming from. For example, a detailed description on this for RedCaps can be found [here](https://huggingface.co/datasets/red_caps). | |
| ## Additional Information | |
| ### Dataset Curators | |
| The authors of the original dataset papers, as well as the authors of the FLAVA paper (Amanpreet, Ronghang, Vedanuj, Guillaume, Wojciech, Marcus and Douwe). | |
| ### Licensing Information | |
| Here are the individual licenses from each of the datasets that apply if you use this dataset: | |
| #### COCO | |
| The annotations in the COCO dataset belong to the COCO Consortium and are licensed under a Creative Commons Attribution 4.0 License. | |
| The COCO Consortium does not own the copyright of the images. Use of the images must abide by the Flickr Terms of Use. The users of the images accept full responsibility for the use of the dataset, including but not limited to the use of any copies of copyrighted images that they may create from the dataset. | |
| #### Conceptual Captions | |
| 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. | |
| #### WIT | |
| This data is available under the [Creative Commons Attribution-ShareAlike 3.0 Unported](LICENSE) license. | |
| #### Visual Genome | |
| Visual Genome by Ranjay Krishna et al is licensed under a Creative Commons Attribution 4.0 International License. | |
| #### Localized Narratives | |
| All the annotations available through this website are released under a [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. You are free to redistribute and modify the annotations, but we ask you to please keep the original attribution to our paper. | |
| #### YFCC100M | |
| Use of the original media files is subject to the Creative Commons licenses chosen by their creators/uploaders. License information for each media file can be found within [the YFCC100M metadata](https://multimediacommons.wordpress.com/yfcc100m-core-dataset/#yfcc100m). Use of the dataset is subject to the relevant Webscope License Agreement, which you need to agree to if you use this dataset. | |
| #### RedCaps | |
| The image metadata is licensed under CC-BY 4.0 license. Additionally, uses of this dataset are subject to Reddit API terms (https://www.reddit.com/wiki/ | |
| api-terms) and users must comply with Reddit User Agreeement, Content Policy, | |
| and Privacy Policy – all accessible at https://www.redditinc.com/policies. | |
| Similar to RedCaps: | |
| > PMD should only be used for non-commercial research. PMD should not be used for any tasks that involve identifying features related to people (facial recognition, gender, age, ethnicity identification, etc.) or make decisions that impact people (mortgages, job applications, criminal sentences; or moderation decisions about user-uploaded data that could result in bans from a website). Any commercial and for-profit uses of PMD are restricted – it should not be used to train models that will be deployed in production systems as part of a product offered by businesses or government agencies. | |
| ### Citation Information | |
| Please cite the main FLAVA paper in which PMD was introduced along with each of the subsets used in PMD as follows: | |
| ```bibtex | |
| @inproceedings{singh2022flava, | |
| title={Flava: A foundational language and vision alignment model}, | |
| author={Singh, Amanpreet and Hu, Ronghang and Goswami, Vedanuj and Couairon, Guillaume and Galuba, Wojciech and Rohrbach, Marcus and Kiela, Douwe}, | |
| booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, | |
| pages={15638--15650}, | |
| year={2022} | |
| } | |
| @article{chen2015microsoft, | |
| title={Microsoft coco captions: Data collection and evaluation server}, | |
| author={Chen, Xinlei and Fang, Hao and Lin, Tsung-Yi and Vedantam, Ramakrishna and Gupta, Saurabh and Doll{\'a}r, Piotr and Zitnick, C Lawrence}, | |
| journal={arXiv preprint arXiv:1504.00325}, | |
| year={2015} | |
| } | |
| @inproceedings{ordonez2011sbucaptions, | |
| Author = {Vicente Ordonez and Girish Kulkarni and Tamara L. Berg}, | |
| Title = {Im2Text: Describing Images Using 1 Million Captioned Photographs}, | |
| Booktitle = {Neural Information Processing Systems ({NIPS})}, | |
| Year = {2011}, | |
| } | |
| @article{krishna2017visual, | |
| title={Visual genome: Connecting language and vision using crowdsourced dense image annotations}, | |
| author={Krishna, Ranjay and Zhu, Yuke and Groth, Oliver and Johnson, Justin and Hata, Kenji and Kravitz, Joshua and Chen, Stephanie and Kalantidis, Yannis and Li, Li-Jia and Shamma, David A and others}, | |
| journal={International journal of computer vision}, | |
| volume={123}, | |
| number={1}, | |
| pages={32--73}, | |
| year={2017}, | |
| publisher={Springer} | |
| } | |
| @article{srinivasan2021wit, | |
| title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning}, | |
| author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc}, | |
| journal={arXiv preprint arXiv:2103.01913}, | |
| year={2021} | |
| } | |
| @inproceedings{sharma2018conceptual, | |
| title={Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning}, | |
| author={Sharma, Piyush and Ding, Nan and Goodman, Sebastian and Soricut, Radu}, | |
| booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, | |
| pages={2556--2565}, | |
| year={2018} | |
| } | |
| @inproceedings{changpinyo2021conceptual, | |
| 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={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, | |
| pages={3558--3568}, | |
| year={2021} | |
| } | |
| @inproceedings{ponttuset2020localized, | |
| author = {Jordi Pont-Tuset and Jasper Uijlings and Soravit Changpinyo and Radu Soricut and Vittorio Ferrari}, | |
| title = {Connecting Vision and Language with Localized Narratives}, | |
| booktitle = {ECCV}, | |
| year = {2020} | |
| } | |
| @article{thomee2016yfcc100m, | |
| title={YFCC100M: The new data in multimedia research}, | |
| author={Thomee, Bart and Shamma, David A and Friedland, Gerald and Elizalde, Benjamin and Ni, Karl and Poland, Douglas and Borth, Damian and Li, Li-Jia}, | |
| journal={Communications of the ACM}, | |
| volume={59}, | |
| number={2}, | |
| pages={64--73}, | |
| year={2016}, | |
| publisher={ACM New York, NY, USA} | |
| } | |
| @misc{desai2021redcaps, | |
| title={PMD: web-curated image-text data created by the people, for the people}, | |
| author={Karan Desai and Gaurav Kaul and Zubin Aysola and Justin Johnson}, | |
| year={2021}, | |
| eprint={2111.11431}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV} | |
| } | |
| ``` | |
| ### Contributions | |
| Thanks to [Thomas Wang](https://huggingface.co/TimeRobber), [@aps](https://github.com/mariosasko) for adding this dataset. |