| --- |
| license: cc0-1.0 |
| pretty_name: OpenHotels2 |
| task_categories: |
| - image-classification |
| - image-feature-extraction |
| - visual-document-retrieval |
| language: |
| - en |
| tags: |
| - hotel-identification |
| - visual-place-recognition |
| - bounding-boxes |
| size_categories: |
| - 100K<n<1M |
| viewer: false |
| extra_gated_prompt: 'By requesting access, you agree not to use OpenHotels-Updated or its |
| associated models to identify, locate, track, profile, or target specific individuals; |
| infer sensitive personal information; facilitate surveillance, harassment, discrimination, |
| exploitation, or other harm; reconstruct or expose information that has been removed, |
| obscured, or anonymized; or create derivative resources that materially increase |
| the risk of misuse without appropriate ethical and safety review. |
| |
| |
| You also agree not to redistribute the resources or access credentials to individuals |
| who have not separately received access. You understand that proposed uses in law |
| enforcement, immigration enforcement, military, intelligence, or commercial surveillance |
| contexts warrant particular scrutiny and should undergo appropriate independent |
| ethical, legal, and institutional review before proceeding. |
| |
| ' |
| extra_gated_fields: |
| I intend to use these resources for: |
| type: select |
| options: |
| - Research |
| - Education |
| - label: Other |
| value: other |
| Briefly describe your intended use: text |
| I have read these usage expectations and agree to use the resources consistently with them: checkbox |
| --- |
| |
| # OpenHotels2 |
|
|
| OpenHotels2 is a benchmark for identifying hotels from images. It contains |
| **474,322 images from 40,974 hotels**, with a gallery and one test set |
| for single-image and multi-image retrieval. Each image includes room-type labels, |
| object labels with bounding boxes, a timestamp, and hotel and visit membership. |
|
|
| | Split | Images | Hotels | Visits | |
| | --- | ---: | ---: | ---: | |
| | gallery | 464,322 | 40,974 | 96,185 | |
| | test | 10,000 | 4,613 | 5,156 | |
|
|
| Every test hotel has images in the gallery, which also contains distractor hotels. |
| There is no training split. The full download is approximately 254 GB in |
| 257 TAR shards containing upright JPEG and PNG images. |
|
|
| ## Download and use |
|
|
| Request access on the dataset page, then install the dependencies and sign in: |
|
|
| ```bash |
| pip install huggingface_hub Pillow |
| hf auth login |
| ``` |
|
|
| Download the release: |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| snapshot_download("imagingforgood/OpenHotels2", repo_type="dataset", |
| local_dir="OpenHotels2") |
| ``` |
|
|
| Run this from the downloaded `OpenHotels2` directory: |
|
|
| ```python |
| from load_openhotels import OpenHotels2 |
| |
| dataset = OpenHotels2(".") |
| hotel_id, visit_id, path, annotation = next(dataset.iter_images("test")) |
| image = dataset.load_image(path) # returns a Pillow image |
| print(hotel_id, annotation["room_labels"], annotation["objects"]) |
| |
| # Use dataset.iter_images("gallery") to iterate the reference images. |
| ``` |
|
|
| The loader reads images directly from the TAR shards; extraction is unnecessary. |
| Loading the metadata and index requires several GB of RAM. To download only test |
| images and their metadata, add |
| `allow_patterns=["*.json", "*.py", "README.md", "shards/test-*.tar"]` to |
| `snapshot_download`. Retrieval evaluation requires the gallery as well. |
|
|
| ## Annotations and files |
|
|
| - `dataset.json`: metadata grouped as `hotels[hotel_id].visits[visit_id].images[image_path]`, |
| including hotel names, coordinates, and image/visit counts. |
| - `shards/`: image archives; `shard_index.json` maps image paths to their archive locations. |
| - `load_openhotels.py`: the Python loader used above (Python 3.10+). |
| - `release_summary.json`: dataset counts and label distributions. Additional manifests, |
| checksums, and validation results describe the packaged files. |
|
|
| Each image has `timestamp`, `width`, `height`, `room_labels`, and `objects`. |
| `objects` maps each label to a list of `[x_min, y_min, x_max, y_max]` boxes in pixel |
| coordinates. Images are already upright, and dimensions and boxes refer to those |
| pixels. Multiple room labels are possible, including `ambiguous`. |
|
|
| Use the visit's `split` field for gallery/test membership; path names may contain |
| legacy split names. Observed room types are in `room_labels`. |
|
|
| ## Evaluation tasks |
|
|
| - **Single-image hotel retrieval:** use each test image as a query and rank gallery |
| images. A result matches when its hotel ID equals the query's hotel ID. Report |
| Recall@K: the fraction of queries with at least one matching image in the top K. |
| - **Multi-image hotel retrieval:** combine two or more test images from the same |
| hotel or visit into a query set, then retrieve from the gallery. Report the |
| grouping, query-set sizes, aggregation method, and set-level Recall@K. |
| - **Performance by scene or object:** break down retrieval results using |
| `room_labels` and object labels to compare performance across image content. |
|
|
| Evaluate on the provided test split, and report the dataset revision and retrieval |
| protocol. Bounding boxes support object-focused analysis, but annotations should |
| not be assumed exhaustive for detection or instance-count evaluation. |
|
|
| ## License and access |
|
|
| Released under [CC0-1.0](https://creativecommons.org/publicdomain/zero/1.0/). |
| Access requires accepting the intended-use terms on the dataset page. |
|
|
