---
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). Digits 5 and 6 of an image name (e.g., `01` for `200001.jpg`).

## Dataset Splits

- **queries**: Query images with modifications for evaluation. Also includes the original images.
- **database**: Original images used as the database for retrieval.

To tell if something is an original image or a strongly modified image, refer to a given images `split_type` field. An example is shown in the `Example Usage` below.

## Dataset Versions

- Version 1.0.0

## Example Usage

Use the Hugging Face `datasets` library to load one of the configs:

```python
import datasets

# Name of the dataset
dataset_name = "randall-lab/INRIA-CopyDays"

# Load query images
query_dataset = datasets.load_dataset(
    dataset_name,
    split="queries",
    trust_remote_code=True,
)

# Load database images
db_dataset = datasets.load_dataset(
    dataset_name,
    split="database",
    trust_remote_code=True,
)

# Print the length of the query dataset -- should be 386, since it includes all 229 strong AND all 157 original queries
print(f"Number of query images: {len(query_dataset)}")

# You can tell if it is a strong or an original query by checking the `split_type` field on a given image
example_query = query_dataset[0]  # Get any desired query image
print(f"Example Query - Filename: {example_query['filename']}")
print(f"Example Query - Split Type: {example_query['split_type']}")

# Print the length of the database dataset -- should be 157, since it includes all 157 original images
print(f"Number of database images: {len(db_dataset)}")
```

## 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)