README.md

---
license: cc0-1.0
task_categories:
- image-retrieval
- image-classification
language:
- en
tags:
- hotel-identification
- image-retrieval
- visual-place-recognition
- object-centric-retrieval
pretty_name: OpenHotels
size_categories:
- 100K<n<1M
---

# OpenHotels

OpenHotels is a large-scale hotel image retrieval benchmark built from hotel-room imagery and associated hotel metadata. The dataset is designed for hotel-scale retrieval: given a query image, a system must retrieve the matching hotel from a large gallery containing both true matching classes and many distractor hotel classes.

## Dataset Structure

The release contains image files under `images/` and four metadata files:

```text
images/
  gallery/
  test_non_object/
  test_object/
metadata_gallery.json
metadata_test_non_object.json
metadata_test_object.json
metadata_hotels.json
```

The benchmark has three image subsets:

- `gallery`: searchable reference images for all hotel classes, including distractor-only hotels.
- `test_non_object`: held-out room-view query images.
- `test_object`: held-out object-centric query images.

## Statistics

| Subset | Hotels | Images |
| --- | ---: | ---: |
| Gallery | 41,027 | 253,597 |
| Test Non-Object | 15,982 | 62,706 |
| Test Object | 4,707 | 54,842 |

The gallery contains 140,247 non-object room-view images and 113,350 object-centric images. Across both test subsets there are 15,982 unique hotel classes; the gallery includes 25,045 distractor hotel classes that do not appear in either test subset.

## Metadata Schema

### `metadata_gallery.json`

Each row describes one gallery image.

```json
{
  "path": "images/gallery/23297/0011.jpg",
  "hotel_id": "23297",
  "room": "244",
  "timestamp": "2024-12-27T04:20:21",
  "is_object": false,
  "view_type": "bedroom"
}
```

If `is_object` is `true`, the row contains `object_type`. If `is_object` is `false`, the row contains `view_type`.

### `metadata_test_non_object.json`

Each row describes one non-object query image.

```json
{
  "path": "images/test_non_object/000000.jpg",
  "hotel_id": "03875",
  "room": "405",
  "timestamp": "2016-06-25T06:13:23",
  "view_type": "bedroom"
}
```

### `metadata_test_object.json`

Each row describes one object-centric query image.

```json
{
  "path": "images/test_object/000000.jpg",
  "hotel_id": "03875",
  "room": "425",
  "timestamp": "2021-07-28T09:43:57",
  "object_type": "nightstand"
}
```

### `metadata_hotels.json`

Each row describes one hotel class.

```json
{
  "hotel_id": "00000",
  "name": "Extended Stay America - Fairbanks - Old Airport Way",
  "lat": 64.83538,
  "lng": -147.8233,
  "date_added": "2015-06-25T21:34:48",
  "in_gallery": true,
  "in_test_non_object": false,
  "in_test_object": false
}
```

## Evaluation Protocol

Use the gallery as the retrieval database. Evaluate the two query subsets separately:

- Test Non-Object evaluates retrieval from room-level query images.
- Test Object evaluates retrieval from object-centric query images.

For each query, rank gallery images or aggregate ranked images to hotel-level predictions, then score retrieval against the query `hotel_id`. The standard metrics are Recall@K for `K in {1, 5, 10, 100}`.

## Citation

TODO: Add the paper citation when available.