|
--- |
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annotations_creators: |
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- found |
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language_creators: |
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- found |
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language: |
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- en |
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license: |
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- unknown |
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multilinguality: |
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- monolingual |
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size_categories: |
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- 1M<n<10M |
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source_datasets: |
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- original |
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task_categories: |
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- image-to-text |
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task_ids: |
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- image-captioning |
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paperswithcode_id: sbu-captions-dataset |
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pretty_name: SBU Captioned Photo Dataset |
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dataset_info: |
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features: |
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- name: image_url |
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dtype: string |
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- name: user_id |
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dtype: string |
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- name: caption |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 143795586 |
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num_examples: 1000000 |
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download_size: 49787719 |
|
dataset_size: 143795586 |
|
--- |
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|
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# Dataset Card for SBU Captioned Photo Dataset |
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## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Dataset Preprocessing](#dataset-preprocessing) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Dataset Creation](#dataset-creation) |
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- [Curation Rationale](#curation-rationale) |
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- [Source Data](#source-data) |
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- [Annotations](#annotations) |
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- [Personal and Sensitive Information](#personal-and-sensitive-information) |
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- [Considerations for Using the Data](#considerations-for-using-the-data) |
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- [Social Impact of Dataset](#social-impact-of-dataset) |
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- [Discussion of Biases](#discussion-of-biases) |
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- [Other Known Limitations](#other-known-limitations) |
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- [Additional Information](#additional-information) |
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- [Dataset Curators](#dataset-curators) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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- [Contributions](#contributions) |
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## Dataset Description |
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- **Homepage:** https://www.cs.rice.edu/~vo9/sbucaptions/ |
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- **Repository:** |
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- **Paper:** [Im2Text: Describing Images Using 1 Million Captioned Photographs](https://papers.nips.cc/paper/2011/hash/5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html) |
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- **Leaderboard:** |
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- **Point of Contact:** [Vicente Ordóñez Román](mailto:[email protected]) |
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### Dataset Summary |
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SBU Captioned Photo Dataset is a collection of associated captions and images from Flickr. |
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### Dataset Preprocessing |
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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: |
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```python |
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from concurrent.futures import ThreadPoolExecutor |
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from functools import partial |
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import io |
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import urllib |
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import PIL.Image |
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from datasets import load_dataset |
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from datasets.utils.file_utils import get_datasets_user_agent |
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USER_AGENT = get_datasets_user_agent() |
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def fetch_single_image(image_url, timeout=None, retries=0): |
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for _ in range(retries + 1): |
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try: |
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request = urllib.request.Request( |
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image_url, |
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data=None, |
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headers={"user-agent": USER_AGENT}, |
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) |
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with urllib.request.urlopen(request, timeout=timeout) as req: |
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image = PIL.Image.open(io.BytesIO(req.read())) |
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break |
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except Exception: |
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image = None |
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return image |
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def fetch_images(batch, num_threads, timeout=None, retries=0): |
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fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries) |
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with ThreadPoolExecutor(max_workers=num_threads) as executor: |
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batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"])) |
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return batch |
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num_threads = 20 |
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dset = load_dataset("sbu_captions") |
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dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads}) |
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``` |
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### Supported Tasks and Leaderboards |
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- `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. |
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### Languages |
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All captions are in English. |
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## Dataset Structure |
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### Data Instances |
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Each instance in SBU Captioned Photo Dataset represents a single image with a caption and a user_id: |
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``` |
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{ |
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'img_url': 'http://static.flickr.com/2723/4385058960_b0f291553e.jpg', |
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'user_id': '47889917@N08', |
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'caption': 'A wooden chair in the living room' |
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} |
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``` |
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### Data Fields |
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- `image_url`: Static URL for downloading the image associated with the post. |
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- `caption`: Textual description of the image. |
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- `user_id`: Author of caption. |
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### Data Splits |
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All the data is contained in training split. The training set has 1M instances. |
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## Dataset Creation |
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### Curation Rationale |
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From the paper: |
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> 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 |
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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. |
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### Source Data |
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The source images come from Flickr. |
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#### Initial Data Collection and Normalization |
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One key contribution of our paper is a novel web-scale database of photographs with associated |
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descriptive text. To enable effective captioning of novel images, this database must be good in two |
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ways: 1) It must be large so that image based matches to a query are reasonably similar, 2) The |
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captions associated with the data base photographs must be visually relevant so that transferring |
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captions between pictures is useful. To achieve the first requirement we query Flickr using a huge |
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number of pairs of query terms (objects, attributes, actions, stuff, and scenes). This produces a very |
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large, but noisy initial set of photographs with associated text. |
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#### Who are the source language producers? |
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The Flickr users. |
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### Annotations |
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#### Annotation process |
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Text descriptions associated with the images are inherited as annotations/captions. |
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#### Who are the annotators? |
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The Flickr users. |
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### Personal and Sensitive Information |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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### Discussion of Biases |
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### Other Known Limitations |
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## Additional Information |
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### Dataset Curators |
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Vicente Ordonez, Girish Kulkarni and Tamara L. Berg. |
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### Licensing Information |
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Not specified. |
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### Citation Information |
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|
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```bibtex |
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@inproceedings{NIPS2011_5dd9db5e, |
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author = {Ordonez, Vicente and Kulkarni, Girish and Berg, Tamara}, |
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booktitle = {Advances in Neural Information Processing Systems}, |
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editor = {J. Shawe-Taylor and R. Zemel and P. Bartlett and F. Pereira and K.Q. Weinberger}, |
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pages = {}, |
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publisher = {Curran Associates, Inc.}, |
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title = {Im2Text: Describing Images Using 1 Million Captioned Photographs}, |
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url = {https://proceedings.neurips.cc/paper/2011/file/5dd9db5e033da9c6fb5ba83c7a7ebea9-Paper.pdf}, |
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volume = {24}, |
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year = {2011} |
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} |
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``` |
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### Contributions |
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|
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Thanks to [@thomasw21](https://github.com/thomasw21) for adding this dataset |
