Update README.md

README.md

@@ -8,7 +8,108 @@ pretty_name: icir
 size_categories:
 - 100K<n<1M
 ---
+## i-CIR Dataset (Hugging Face)
 
 <p align="center">
 <img width="75%" alt="i-CIR illustration" src="https://github.com/billpsomas/icir/raw/main/.github/dataset.png">
-</p>
+</p>
+
+### About
+**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).
+
+**Key stats**
+- **Instances:** 202  
+- **Total images:** ~750K  
+- **Composed queries:** 1,883  
+- **Avg database size / query:** ~3.7K images  
+- Includes challenging hard negatives per instance.
+
+---
+
+### Dataset Structure
+On Hugging Face, i-CIR is hosted as **WebDataset shards** for scalable/robust downloads and streaming.
+
+```text
+icir/
+├── webdataset/
+│   ├── query/
+│   │   ├── query-000000.tar
+│   │   ├── query-000001.tar
+│   │   └── ...
+│   └── database/
+│       ├── database-000000.tar
+│       ├── database-000001.tar
+│       └── ...
+├── annotations/
+│   ├── query_files.csv
+│   ├── database_files.csv
+├── VERSION.txt
+└── LICENSE
+```
+
+---
+
+### Annotations format
+
+- query_files.csv: each row is (image_path, text_query, instance_id)
+- 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)
+
+Inside each WebDataset sample, we store:
+- an image (.jpg/.png/...)
+- a json payload with: img_path, text, instance
+
+---
+
+### Download
+
+One-liner download (recommended):
+
+```bash
+pip install -U huggingface_hub
+huggingface-cli download billpsomas/icir --repo-type dataset --local-dir ./data/icir --revision main
+```
+
+Python (equivalent):
+
+```python
+from huggingface_hub import snapshot_download
+snapshot_download(repo_id="billpsomas/icir", repo_type="dataset", local_dir="./data/icir", revision="main")
+```
+
+---
+
+### Using the dataset (feature extraction)
+
+You can extract features directly from the WebDataset shards (no image folder extraction needed):
+
+```bash
+python3 create_features.py \
+  --dataset icir \
+  --icir_source wds \
+  --icir_wds_root ./data/icir \
+  --backbone clip \
+  --batch 512 \
+  --gpu 0
+```
+
+---
+
+### License
+
+The dataset is released under CC BY-NC-SA 4.0. Please see LICENSE for details.
+
+---
+
+### Citation
+
+If you use i-CIR in your research, please cite:
+
+```
+@inproceedings{
+    psomas2025instancelevel,
+    title={Instance-Level Composed Image Retrieval},
+    author={Bill Psomas and George Retsinas and Nikos Efthymiadis and Panagiotis Filntisis and Yannis Avrithis and Petros Maragos and Ondrej Chum and Giorgos Tolias},
+    booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
+    year={2025}
+}
+```