README.md

metadata

pretty_name: Ecoset
license: cc
source_datasets:
  - original
task_categories:
  - image-classification
  - image
task_ids:
  - multi-class-image-classification
  - other-other-image-classification
  - image-classification
  - other-image-classification
paperswithcode_id: ecoset

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
• Installation
  • Install requirements
  • Download settings
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://www.kietzmannlab.org/ecoset
• Repository: https://codeocean.com/capsule/9570390/tree/v1
• Paper: https://www.pnas.org/doi/full/10.1073/pnas.2011417118
• Point of Contact: tim.kietzmann@uni-osnabrueck.de

Dataset Summary

Tired of all the dogs in ImageNet (ILSVRC)? Then ecoset is here for you. 1.5m images from 565 basic level categories, chosen to be both (i) frequent in linguistic usage, and (ii) rated by human observers as concrete (e.g. ‘table’ is concrete, ‘romance’ is not).

Ecoset is a typical image recognition dataset, combining images of objects with appropriate labels (one label per image). Importantly, ecoset is intended to provide higher ecological validity than its counterparts, with a mislabelling error rate < 5% and filtered for NSFW content. For more information on the dataset, consider reading the original publication.

Ecoset consists of a train, test, and validation subset which all are openly available to the user.

Supported Tasks and Leaderboards

Ecoset is a large multi-class single-label object recognition image dataset (similar to ImageNet).

Install Requirements

In order to work with ecoset, please make sure to install the s3 compatible version of huggingface datasets, which should include the s3fs, botocore and boto3 modules:

pip install datasets[s3]

Download Settings

Please set ignore_verifications=True. when downloading this dataset, else the download will result in an error:

from datasets import load_dataset

dataset = load_dataset("DiGyt/ecoset", ignore_verifications=True)

Dataset Structure

We show detailed information for all the configurations of the dataset. Currently, there is only one setting (Full) available, containing all data.

Data Instances

Full

• Size of downloaded dataset files: 155 GB
• Total amount of disk used: 311 GB

Dataset Creation

A total of 565 categories were selected based on the following: 1) their word frequency in American television and film subtitles (SUBTLEX_US), 2) the perceived concreteness by human observers, and 3) the availability of a minimum of 700 images. Images were sourced via the overall ImageNet database (the same resource used for ILSVRC 2012) or obtained under CC BY-NC-SA 2.0 license from Bing image search and Flickr. Thorough data cleaning procedures were put in place to remove duplicates and to assure an expected misclassification rate per category of <4%.

Curation Rationale

TODO

Source Data

The source data is available under: https://codeocean.com/capsule/9570390/tree/v1

Annotations

Each ecoset image folder is annotated with class labels according to the main object depicted in a class of images. No further annotations are added to the dataset.

Personal and Sensitive Information

The dataset was tested to exclude sensitive images using Yahoo's Open NSFW detection model, removing all image with an NSFW score above 0.8. For this dataset, only images with secured license information was used, which should prevent the inclusion of images without consent of the image's authors and subjects. Despite these measures, it is possible that the images in the dataset contain personal and sensitive information.

Considerations for Using the Data

Social Impact of Dataset

Ecoset is aimed to provide an ecologically valid and perspective on

Discussion of Biases

Despite best efforts to provide an ecologically valid dataset, ecoset is likely to contain biased data. The category selection of ecoset was based on human concreteness ratings and word frequencies in a corpus consisting of American television and film subtitles. This undoubtedly biases the category selection toward Western cultures. Image inclusion was based on the availability via Bing/Flickr search results as well as the existence of relevant ImageNet categories. Images depicting people, specifically the categories “man,” “woman,” and “child,” were not sampled according to census distributions (age, ethnicity, gender, etc.).

Other Known Limitations

In addition to points mentioned in Discussion of Biases, ecoset image and category distributions do not reflect the naturalistic, egocentric visual input typically encountered in the everyday life of infant and adults.

Additional Information

Dataset Curators

The corpus was put together by Johannes Mehrer, Courtney J. Spoerer, Emer C. Jones, Nikolaus Kriegeskorte, and Tim C. Kietzmann.

Licensing Information

Ecoset is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 2.0 license (cc-by-nc-sa-2.0).

Citation Information

@article{mehrer2021ecologically,
  title={An ecologically motivated image dataset for deep learning yields better models of human vision},
  author={Mehrer, Johannes and Spoerer, Courtney J and Jones, Emer C and Kriegeskorte, Nikolaus and Kietzmann, Tim C},
  journal={Proceedings of the National Academy of Sciences},
  volume={118},
  number={8},
  pages={e2011417118},
  year={2021},
  publisher={National Acad Sciences}
}

Contributions

Thanks to #TODO