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
pretty_name: ImageInWords
multilinguality:
  - monolingual

ImageInWords: Unlocking Hyper-Detailed Image Descriptions

arXiv:

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🤗

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="YOUR_HF_ACCESS_TOKEN", name="IIW-400")

Dataset-Explorer

Dataset Description

• Homepage: https://google.github.io/imageinwords/
• Point of Contact: iiw-dataset@google.com

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 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

IIW:

• 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:

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

DOCCI:

• image
• image/url
• 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:

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

CM3600:

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

Please note that all fields are string.

Data Splits

Dataset	Size
IIW	400
DCI	112
DOCCI	100
LocNar	1000
CS3600	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

@inproceedings{Garg2024IIW,
  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},
  title         = {{ImageInWords: Unlocking Hyper-Detailed Image Descriptions}},
  booktitle     = {arXiv},
  year          = {2024}