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 | dataset | arxiv

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

Instructions

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 (coming soon...)
• kNN search (coming soon...)
• evaluation (coming soon...)
• re-ranking with local representations (coming soon...)
• linear adaptation (coming soon...)

Citation

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},
} 

License

The code in this repository is licensed under the MIT License - see the LICENSE for details.

Contact

For more information, inquiries, or further details, please reach out to Giorgos Kordopatis-Zilos