| --- |
| 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. |
|
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| ## 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) |
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|
