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--- |
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license: cc-by-nc-sa-4.0 |
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task_categories: |
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- image-text-to-image |
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language: |
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- en |
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pretty_name: icir |
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size_categories: |
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- 100K<n<1M |
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--- |
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## i-CIR Dataset (Hugging Face) |
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[**website**](https://vrg.fel.cvut.cz/icir/) | [**arxiv**](https://arxiv.org/pdf/2510.25387) | [**github**](https://github.com/billpsomas/icir) |
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### About |
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**i-CIR (Instance-Level Composed Image Retrieval)** is a curated benchmark for **composed image retrieval** where each *instance* corresponds to a specific, visually indistinguishable object (e.g., a particular landmark). Each query combines an **image of the instance** with a **text modification**, and retrieval is evaluated against a database containing **rich hard negatives** (visual / textual / compositional). |
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<p align="center"> |
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<img width="75%" alt="i-CIR illustration" src="https://github.com/billpsomas/icir/raw/main/.github/dataset.png"> |
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</p> |
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**Key stats** |
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- **Instances:** 202 |
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- **Total images:** ~750K |
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- **Composed queries:** 1,883 |
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- **Avg database size / query:** ~3.7K images |
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- Includes challenging hard negatives per instance. |
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--- |
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### Dataset Structure |
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On Hugging Face, i-CIR is hosted as **WebDataset shards** for scalable/robust downloads and streaming. |
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```text |
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icir/ |
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├── webdataset/ |
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│ ├── query/ |
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│ │ ├── query-000000.tar |
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│ │ ├── query-000001.tar |
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│ │ └── ... |
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│ └── database/ |
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│ ├── database-000000.tar |
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│ ├── database-000001.tar |
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│ └── ... |
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├── annotations/ |
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│ ├── query_files.csv |
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│ ├── database_files.csv |
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├── VERSION.txt |
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└── LICENSE |
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``` |
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--- |
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### Annotations format |
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- query_files.csv: each row is (image_path, text_query, instance_id) |
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- database_files.csv: each row is (image_path, text_query, instance_id) (the text field may be unused for database features depending on the pipeline) |
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Inside each WebDataset sample, we store: |
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- an image (.jpg/.png/...) |
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- a json payload with: img_path, text, instance |
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--- |
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### Download |
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One-liner download (recommended): |
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```bash |
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pip install -U huggingface_hub |
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huggingface-cli download billpsomas/icir --repo-type dataset --local-dir ./data/icir --revision main |
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``` |
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Python (equivalent): |
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```python |
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from huggingface_hub import snapshot_download |
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snapshot_download(repo_id="billpsomas/icir", repo_type="dataset", local_dir="./data/icir", revision="main") |
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``` |
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--- |
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### Using the dataset (feature extraction) |
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You can extract features directly from the WebDataset shards (no image folder extraction needed): |
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```bash |
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python3 create_features.py \ |
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--dataset icir \ |
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--icir_source wds \ |
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--icir_wds_root ./data/icir \ |
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--backbone clip \ |
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--batch 512 \ |
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--gpu 0 |
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``` |
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--- |
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### License |
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The dataset is released under CC BY-NC-SA 4.0. Please see LICENSE for details. |
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--- |
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### Citation |
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If you use i-CIR in your research, please cite: |
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``` |
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@inproceedings{ |
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psomas2025instancelevel, |
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title={Instance-Level Composed Image Retrieval}, |
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author={Bill Psomas and George Retsinas and Nikos Efthymiadis and Panagiotis Filntisis and Yannis Avrithis and Petros Maragos and Ondrej Chum and Giorgos Tolias}, |
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booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems}, |
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year={2025} |
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} |
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``` |
