README.md

metadata

annotations_creators:
  - expert-generated
  - crowdsourced
license: cc-by-4.0
task_categories:
  - image-to-text
  - text-to-image
  - object-detection
language:
  - en
size_categories:
  - 1K<n<10K
tags:
  - iiw
  - imageinwords
  - image-descriptions
  - image-captions
  - detailed-descriptions
  - hyper-detailed-descriptions
  - object-descriptions
  - object-detection
  - object-labels
  - image-text
  - t2i
  - i2t
  - dataset
pretty_name: ImageInWords
multilinguality:
  - monolingual

ImageInWords: Unlocking Hyper-Detailed Image Descriptions

Please visit the webpage for all the information about the IIW project, data downloads, visualizations, and much more.

Please reach out to iiw-dataset@google.com for thoughts/feedback/questions/collaborations.

🤗Hugging Face🤗

IIW-Benchmark Eval Dataset

from datasets import load_dataset

# `name` can be one of: IIW-400, DCI_Test, DOCCI_Test, CM_3600, LocNar_Eval
# refer: https://github.com/google/imageinwords/tree/main/datasets
dataset = load_dataset('google/imageinwords', token=None, name="IIW-400", trust_remote_code=True)

Dataset-Explorer

Dataset Description

• Paper: arXiv
• Homepage: https://google.github.io/imageinwords/
• Point of Contact: iiw-dataset@google.com
• Dataset Explorer: ImageInWords-Explorer

Dataset Summary

ImageInWords (IIW), a carefully designed human-in-the-loop annotation framework for curating hyper-detailed image descriptions and a new dataset resulting from this process. We validate the framework through evaluations focused on the quality of the dataset and its utility for fine-tuning with considerations for readability, comprehensiveness, specificity, hallucinations, and human-likeness.

This Data Card describes IIW-Benchmark: Eval Datasets, a mixture of human annotated and machine generated data intended to help create and capture rich, hyper-detailed image descriptions.

IIW dataset has two parts: human annotations and model outputs. The main purposes of this dataset are:

1. to provide samples from SoTA human authored outputs to promote discussion on annotation guidelines to further improve the quality
2. to provide human SxS results and model outputs to promote development of automatic metrics to mimic human SxS judgements.

Supported Tasks

Text-to-Image, Image-to-Text, Object Detection

Languages

English

Dataset Structure

Data Instances

Data Fields

For details on the datasets and output keys, please refer to our GitHub data page inside the individual folders.

IIW-400:

• image/key
• image/url
• IIW: Human generated image description
• IIW-P5B: Machine generated image description
• iiw-human-sxs-gpt4v and iiw-human-sxs-iiw-p5b: human SxS metrics
  • metrics/Comprehensiveness
  • metrics/Specificity
  • metrics/Hallucination
  • metrics/First few line(s) as tldr
  • metrics/Human Like

DCI_Test:

• image
• image/url
• ex_id
• IIW: Human authored image description
• metrics/Comprehensiveness
• metrics/Specificity
• metrics/Hallucination
• metrics/First few line(s) as tldr
• metrics/Human Like

DOCCI_Test:

• image
• image/thumbnail_url
• IIW: Human generated image description
• DOCCI: Image description from DOCCI
• metrics/Comprehensiveness
• metrics/Specificity
• metrics/Hallucination
• metrics/First few line(s) as tldr
• metrics/Human Like

LocNar_Eval:

• image/key
• image/url
• IIW-P5B: Machine generated image description

CM_3600:

• image/key
• image/url
• IIW-P5B: Machine generated image description

Please note that all fields are string.

Data Splits

Dataset	Size
IIW-400	400
DCI_Test	112
DOCCI_Test	100
LocNar_Eval	1000
CM_3600	1000

Annotations

Annotation process

Some text descriptions were written by human annotators and some were generated by machine models. The metrics are all from human SxS.

Personal and Sensitive Information

The images that were used for the descriptions and the machine generated text descriptions are checked (by algorithmic methods and manual inspection) for S/PII, pornographic content, and violence and any we found may contain such information have been filtered. We asked that human annotators use an objective and respectful language for the image descriptions.

Licensing Information

CC BY 4.0

Citation Information

@misc{garg2024imageinwords,
      title={ImageInWords: Unlocking Hyper-Detailed Image Descriptions}, 
      author={Roopal Garg and Andrea Burns and Burcu Karagol Ayan and Yonatan Bitton and Ceslee Montgomery and Yasumasa Onoe and Andrew Bunner and Ranjay Krishna and Jason Baldridge and Radu Soricut},
      year={2024},
      eprint={2405.02793},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}