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---
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/)
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