|
|
--- |
|
|
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) |
|
|
|
