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.
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
You can find instructions for downloading ILIAS and relevant code for the various processes related to our benchmark in the links below
- download ILIAS
- feature extraction
- kNN search
- evaluation
- re-ranking with local representations (coming soon...)
- linear adaptation (coming soon...)
If you use ILIAS in your research or find our work helpful, please consider citing our paper and starring this repo
@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},
} The code in this repository is licensed under the MIT License - see the LICENSE for details.
For more information, inquiries, or further details, please reach out to Giorgos Kordopatis-Zilos