---
language: en
tags:
  - image-retrieval
  - oxford5k
  - paris6k
  - revisitop1m
---

# Dataset Card for RevisitOP (Oxford5k, Paris6k, RevisitOP1M)

## Dataset Description

**RevisitOP** provides popular benchmark datasets for large-scale image retrieval research:

- **roxford5k**: Oxford 5k buildings dataset containing ~5,000 images.
- **rparis6k**: Paris 6k buildings dataset with ~6,000 images.
- **revisitop1m**: RevisitOP 1M distractor dataset with ~1 million distractor images.
- **oxfordparis**: Combination of Oxford 5k and Paris 6k datasets.

These datasets are widely used for evaluating image retrieval algorithms and contain real-world building photographs and distractors.

## Dataset Features

Each example contains:

- `image` (`Image`): An image file (JPEG or PNG).
- `filename` (`string`): The original filename of the image.
- `dataset` (`string`): The source dataset the image belongs to (`roxford5k`, `rparis6k`, or `revisitop1m`).
- `query_id` (`int32`): Query ID for query images (-1 for database images).
- `bbx` (`Sequence[float32]`): Bounding box coordinates [x1, y1, x2, y2] for query images.
- `easy` (`Sequence[int32]`): Easy relevant images for queries.
- `hard` (`Sequence[int32]`): Hard relevant images for queries.
- `junk` (`Sequence[int32]`): Junk images for queries.

## Dataset Splits

- **qimlist**: Query images with ground truth annotations (bounding boxes and relevance labels).
- **imlist**: Database images 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
from aiohttp import ClientTimeout

dataset_name = "randall-lab/revisitop"
timeout_period = 500000  # very long timeout to prevent timeouts
storage_options = {"client_kwargs": {"timeout": ClientTimeout(total=timeout_period)}}

# These are the config names defined in the script
dataset_configs = ["roxford5k", "rparis6k", "oxfordparis"]  # "revisitop1m" is large and may take a long time to load

# Load query split for evaluation
for i, config_name in enumerate(dataset_configs, start=1):
    # Load query images
    query_dataset = datasets.load_dataset(
        path=dataset_name,
        name=config_name,
        split="qimlist",
        trust_remote_code=True,
        storage_options=storage_options,
    )

    # Load database images
    db_dataset = datasets.load_dataset(
        path=dataset_name,  
        name=config_name,
        split="imlist",
        trust_remote_code=True,
        storage_options=storage_options,
    )


    # Example query image
    query_example = query_dataset[0]
```

## Dataset Structure

- The datasets consist of images downloaded and extracted from official URLs hosted by the Oxford Visual Geometry Group and the RevisitOP project.
- The `roxford5k` and `rparis6k` datasets come from `.tgz` archives with corresponding `.pkl` ground truth files.
- The `revisitop1m` dataset consists of 100 `.tar.gz` archives with JPEG images as distractors.
- The combined `oxfordparis` dataset merges the Oxford and Paris sets.
- Ground truth files contain query lists, database lists, and annotations (bounding boxes, easy/hard/junk labels).

## Dataset Citation

If you use this dataset, please cite the original paper:

```bibtex
@inproceedings{Radenovic2018RevisitingOP,
  title={Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking},
  author={Filip Radenovic and Ahmet Iscen and Giorgos Tolias and Yannis Avrithis and Ondrej Chum},
  year={2018}
}
```

## Dataset Homepage

[RevisitOP project page](http://cmp.felk.cvut.cz/revisitop/)