| --- |
| annotations_creators: |
| - found |
| language_creators: |
| - found |
| language: |
| - en |
| license: |
| - unknown |
| multilinguality: |
| - monolingual |
| size_categories: |
| - 1M<n<10M |
| source_datasets: |
| - original |
| task_categories: |
| - image-to-text |
| task_ids: |
| - image-captioning |
| paperswithcode_id: sbu-captions-dataset |
| pretty_name: SBU Captioned Photo Dataset |
| dataset_info: |
| features: |
| - name: image_url |
| dtype: string |
| - name: user_id |
| dtype: string |
| - name: caption |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 143795586 |
| num_examples: 1000000 |
| download_size: 49787719 |
| dataset_size: 143795586 |
| --- |
| |
| # Dataset Card for SBU Captioned Photo Dataset |
|
|
| ## 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:** https://www.cs.rice.edu/~vo9/sbucaptions/ |
| - **Repository:** |
| - **Paper:** [Im2Text: Describing Images Using 1 Million Captioned Photographs](https://papers.nips.cc/paper/2011/hash/5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html) |
| - **Leaderboard:** |
| - **Point of Contact:** [Vicente Ordóñez Román](mailto:vicenteor@rice.edu) |
|
|
| ### Dataset Summary |
|
|
| SBU Captioned Photo Dataset is a collection of associated captions and images from Flickr. |
|
|
| ### 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("sbu_captions") |
| dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads}) |
| ``` |
|
|
| ### Supported Tasks and Leaderboards |
|
|
| - `image-to-text`: This dataset can be used to train a model for Image Captioning where the goal is to predict a caption given the image. |
|
|
| ### Languages |
|
|
| All captions are in English. |
|
|
| ## Dataset Structure |
|
|
| ### Data Instances |
|
|
| Each instance in SBU Captioned Photo Dataset represents a single image with a caption and a user_id: |
| |
| ``` |
| { |
| 'img_url': 'http://static.flickr.com/2723/4385058960_b0f291553e.jpg', |
| 'user_id': '47889917@N08', |
| 'caption': 'A wooden chair in the living room' |
| } |
| ``` |
| |
| ### Data Fields |
| |
| - `image_url`: Static URL for downloading the image associated with the post. |
| - `caption`: Textual description of the image. |
| - `user_id`: Author of caption. |
| |
| ### Data Splits |
| |
| All the data is contained in training split. The training set has 1M instances. |
| |
| ## Dataset Creation |
| |
| ### Curation Rationale |
| |
| From the paper: |
| > One contribution is our technique for the automatic collection of this new dataset – performing a huge number of Flickr queries and then filtering the noisy results down to 1 million images with associated visually |
| relevant captions. Such a collection allows us to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results. |
| |
| ### Source Data |
| |
| The source images come from Flickr. |
| |
| #### Initial Data Collection and Normalization |
| |
| One key contribution of our paper is a novel web-scale database of photographs with associated |
| descriptive text. To enable effective captioning of novel images, this database must be good in two |
| ways: 1) It must be large so that image based matches to a query are reasonably similar, 2) The |
| captions associated with the data base photographs must be visually relevant so that transferring |
| captions between pictures is useful. To achieve the first requirement we query Flickr using a huge |
| number of pairs of query terms (objects, attributes, actions, stuff, and scenes). This produces a very |
| large, but noisy initial set of photographs with associated text. |
| |
| #### Who are the source language producers? |
| |
| The Flickr users. |
| |
| ### Annotations |
| |
| #### Annotation process |
| |
| Text descriptions associated with the images are inherited as annotations/captions. |
| |
| #### Who are the annotators? |
| |
| The Flickr users. |
| |
| ### Personal and Sensitive Information |
| |
| ## Considerations for Using the Data |
| |
| ### Social Impact of Dataset |
| |
| ### Discussion of Biases |
| |
| ### Other Known Limitations |
| |
| ## Additional Information |
| |
| ### Dataset Curators |
| |
| Vicente Ordonez, Girish Kulkarni and Tamara L. Berg. |
| |
| ### Licensing Information |
| |
| Not specified. |
| |
| ### Citation Information |
| |
| ```bibtex |
| @inproceedings{NIPS2011_5dd9db5e, |
| author = {Ordonez, Vicente and Kulkarni, Girish and Berg, Tamara}, |
| booktitle = {Advances in Neural Information Processing Systems}, |
| editor = {J. Shawe-Taylor and R. Zemel and P. Bartlett and F. Pereira and K.Q. Weinberger}, |
| pages = {}, |
| publisher = {Curran Associates, Inc.}, |
| title = {Im2Text: Describing Images Using 1 Million Captioned Photographs}, |
| url = {https://proceedings.neurips.cc/paper/2011/file/5dd9db5e033da9c6fb5ba83c7a7ebea9-Paper.pdf}, |
| volume = {24}, |
| year = {2011} |
| } |
| ``` |
| |
| ### Contributions |
| |
| Thanks to [@thomasw21](https://github.com/thomasw21) for adding this dataset |
