Create README.md

README.md

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+---
+language: en
+tags:
+  - image-retrieval
+  - copydays
+---
+
+# Dataset Card for Copydays
+
+## Dataset Description
+
+**Copydays** is a dataset designed for evaluating copy detection and near-duplicate image retrieval algorithms. It contains images with various modifications to test the robustness of retrieval systems.
+
+- **copydays_original**: Original, unmodified images.
+- **copydays_strong**: Images with strong modifications (e.g., cropping, rotation, compression).
+
+These datasets are widely used for benchmarking image retrieval systems under challenging conditions.
+
+## Dataset Features
+
+Each example contains:
+
+- `image` (`Image`): An image file (JPEG or PNG).
+- `filename` (`string`): The original filename of the image (e.g., `200000.jpg`).
+- `split_type` (`string`): The type of split the image belongs to (`original` or `strong`).
+- `block_id` (`int32`): The first 4 digits of the filename, representing the block ID (e.g., `2000` for `200000.jpg`).
+- `query_id` (`int32`): The query ID for query images (-1 for database images).
+
+## Dataset Splits
+
+- **queries**: Query images with modifications for evaluation.
+- **database**: Original images used as the database for retrieval.
+
+## Dataset Versions
+
+- Version 1.0.0
+
+## Example Usage
+
+Use the Hugging Face `datasets` library to load one of the configs:
+
+```python
+import datasets
+
+# These are the config names defined in the script
+dataset_configs = ["database", "query"]
+
+# Load query split for evaluation
+for i, config_name in enumerate(dataset_configs, start=1):
+    # Load query images
+    query_dataset = datasets.load_dataset(
+        path="copydays.py",  # use local dataset script
+        name=config_name,
+        split="queries",
+        trust_remote_code=True,
+    )
+
+    # Load database images
+    db_dataset = datasets.load_dataset(
+        path="copydays.py",  # use local dataset script
+        name=config_name,
+        split="database",
+        trust_remote_code=True,
+    )
+
+    print(f"[{i}] Loaded config '{config_name}' - queries: {len(query_dataset)}, database: {len(db_dataset)}")
+
+    # Example query
+    query_example = query_dataset[0]
+    print(f"Query: {query_example['filename']}, Block ID: {query_example['block_id']}, Query ID: {query_example['query_id']}")
+```
+
+## Dataset Structure
+
+- The datasets consist of images downloaded and extracted from official URLs hosted by the Copydays project.
+- The `copydays_original` dataset contains unmodified images.
+- The `copydays_strong` dataset contains images with strong modifications.
+
+## Dataset Citation
+
+If you use this dataset, please cite the original paper:
+
+```bibtex
+@inproceedings{jegou2008hamming,
+  title={Hamming embedding and weak geometric consistency for large scale image search},
+  author={Jegou, Herve and Douze, Matthijs and Schmid, Cordelia},
+  booktitle={European conference on computer vision},
+  pages={304--317},
+  year={2008},
+  organization={Springer}
+}
+```
+
+## Dataset Homepage
+
+[Copydays project page](https://thoth.inrialpes.fr/~jegou/data.php.html#copydays)