Datasets:
image imagewidth (px) 400 7.56k |
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YAML Metadata Warning:The task_categories "image-retrieval" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
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.
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