README.md

metadata

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:

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.

{
  "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.

{
  "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.

{
  "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.

{
  "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.