Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
License:
File size: 3,260 Bytes
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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.
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