Add files using upload-large-folder tool

README.md

@@ -1,3 +1,128 @@
----
-license: cc0-1.0
----
+---
+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.