Datasets:

vicenteor
/

sbu_captions

	---
	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 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: [SBU Captioned Photo Dataset homepage](http://www.cs.virginia.edu/~vicente/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:vicente@virginia.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.