vrg-prague/ilias

ILIAS is a large-scale test dataset for evaluation on Instance-Level Image retrieval At Scale. It is designed to support future research in image-to-image and text-to-image retrieval for particular objects and serves as a benchmark for evaluating representations of foundation or customized vision and vision-language models, as well as specialized retrieval techniques.

website | download | arxiv | github

Composition

The dataset includes 1,000 object instances across diverse domains, with:

• 5,947 images in total:
  • 1,232 image queries, depicting query objects on clean or uniform background
  • 4,715 positive images, featuring the query objects in real-world conditions with clutter, occlusions, scale variations, and partial views
• 1,000 text queries, providing fine-grained textual descriptions of the query objects
• 100M distractors from YFCC100M to evaluate retrieval performance under large-scale settings, while asserting noise-free ground truth

Dataset details

This repository contains the ILIAS dataset split into the following splits:

• ILIAS core collected by the ILIAS team:
  • 1,232 image queries (img_queries),
  • 4,715 positive images (core_db),
  • 1,000 text queries (text_queries),
• mini set of 5M distractors from YFCC100M (mini_distractors),
• full set of 100M distractors from YFCC100M (distractors_100m).

Loading the dataset

To load the dataset using HugginFace datasets, you first need to pip install datasets, then run the following code:

from datasets import load_dataset

ilias_core_img_queries = load_dataset("vrg-prague/ilias", name="img_queries") # or "text_queries" or "core_db" or "mini_distractors" or "distractors_100m"

Citation

If you use ILIAS in your research or find our work helpful, please consider citing our paper

@inproceedings{ilias2025,
  title={{ILIAS}: Instance-Level Image retrieval At Scale},
  author={Kordopatis-Zilos, Giorgos and Stojnić, Vladan and Manko, Anna and Šuma, Pavel and Ypsilantis, Nikolaos-Antonios and Efthymiadis, Nikos and Laskar, Zakaria and Matas, Jiří and Chum, Ondřej and Tolias, Giorgos},
  booktitle={Computer Vision and Pattern Recognition (CVPR)},
  year={2025},
}