README.md

---
license: cc0-1.0
task_categories:
- image-classification
- image-feature-extraction
- visual-document-retrieval
language:
- en
tags:
- hotel-identification
- image-retrieval
- visual-place-recognition
- object-centric-retrieval
- representative-sample
pretty_name: OpenHotels Representative Sample
size_categories:
- n<1K
configs:
- config_name: image_metadata
  default: true
  data_files:
  - split: gallery
    path: metadata_gallery.json
  - split: test_non_object
    path: metadata_test_non_object.json
  - split: test_object
    path: metadata_test_object.json
- config_name: hotel_metadata
  data_files: metadata_hotels.json
---

# OpenHotels Representative Sample

This repository contains a representative sample of OpenHotels for review and inspection. It mirrors the full OpenHotels release structure: image files are stored in tar shards under `shards/`, and metadata files describe the gallery, non-object query images, object-centric query images, and hotel classes.

The sample is intended for data-quality inspection, not benchmark reporting. Use the full OpenHotels dataset for final evaluation.

## Sampling Procedure

The sample was created deterministically from the full OpenHotels release. We selected hotel classes that appear in all three subsets: the gallery, Test Non-Object, and Test Object. From those eligible hotels, we sorted hotel IDs by a SHA-1 hash and selected the first 20 classes. For each selected class, we included up to 10 gallery images, up to 3 Test Non-Object queries, and up to 3 Test Object queries.

This guarantees that every query image in this sample has a matching hotel class represented in the sample gallery.

## Contents

```text
shards/
  gallery-00000.tar
  test_non_object-00000.tar
  test_object-00000.tar
metadata_gallery.json
metadata_test_non_object.json
metadata_test_object.json
metadata_hotels.json
README.md
```

## Statistics

| Subset | Hotels | Images |
| --- | ---: | ---: |
| Gallery | 20 | 126 |
| Test Non-Object | 20 | 52 |
| Test Object | 20 | 60 |

## Metadata

The sample includes the same metadata fields as the full OpenHotels release.

### Image Metadata

All image metadata rows include:

- `path`: image member name inside the tar file listed in `shard`.
- `shard`: relative path to the tar shard containing the image.
- `hotel_id`: stable anonymized hotel class identifier.
- `room`: room identifier associated with the image upload when available. This is not the semantic room/view label.
- `timestamp`: upload timestamp associated with the image.

Gallery rows additionally include:

- `is_object`: whether the gallery image is object-centric.
- `view_type`: room-level view category for non-object gallery images.
- `object_type`: localized object category for object-centric gallery images.

Test Non-Object rows additionally include:

- `view_type`: room-level view category for the query image.

Test Object rows additionally include:

- `object_type`: localized object category for the query image.

`view_type` describes the room-level view depicted in a non-object image. Possible values in the full dataset are: `bedroom`, `bathroom`, `living area`, `hallway`, `kitchen`, `closet`, and `balcony`.

`object_type` describes the localized hotel-room object depicted in an object-centric image. Possible values in the full dataset are: `bed`, `lamp`, `artwork`, `window/curtains`, `toilet`, `nightstand`, `sink`, `seating`, `office desk`, `shower/bathtub`, `tv`, `wardrobe`, `door`, `chest`, `mirror`, `kitchen appliances`, `flooring`, and `sign`.

### Hotel Metadata

Each row in `metadata_hotels.json` describes one hotel class:

- `hotel_id`: stable anonymized hotel class identifier.
- `name`: hotel name.
- `lat`: hotel latitude in decimal degrees.
- `lng`: hotel longitude in decimal degrees.
- `date_added`: timestamp when the hotel record was added.
- `in_gallery`: whether the hotel appears in the gallery subset.
- `in_test_non_object`: whether the hotel appears in the Test Non-Object subset.
- `in_test_object`: whether the hotel appears in the Test Object subset.

## Loading Images

The same helper can load gallery images and both query subsets. The metadata row tells the loader which tar shard contains the image and which member path to read from that shard.

```python
import json
import tarfile
from io import BytesIO
from PIL import Image

def load_image(row):
    """Load one OpenHotels image from its tar shard."""
    with tarfile.open(row["shard"], "r") as tar:
        image_file = tar.extractfile(row["path"])
        return Image.open(BytesIO(image_file.read())).convert("RGB")

def load_metadata(path):
    with open(path) as f:
        return json.load(f)
```

Load a gallery image. Gallery rows contain `is_object`; non-object gallery rows include `view_type`, and object-centric gallery rows include `object_type`.

```python
gallery = load_metadata("metadata_gallery.json")
gallery_row = gallery[0]
gallery_image = load_image(gallery_row)

print(gallery_row["hotel_id"], gallery_row["path"])
if gallery_row["is_object"]:
    print("object type:", gallery_row["object_type"])
else:
    print("view type:", gallery_row["view_type"])
```

Load a Test Non-Object query image and inspect its room-level view label.

```python
test_non_object = load_metadata("metadata_test_non_object.json")
non_object_row = test_non_object[0]
non_object_image = load_image(non_object_row)

print(non_object_row["hotel_id"], non_object_row["path"])
print("view type:", non_object_row["view_type"])
```

Load a Test Object query image and inspect its object label.

```python
test_object = load_metadata("metadata_test_object.json")
object_row = test_object[0]
object_image = load_image(object_row)

print(object_row["hotel_id"], object_row["path"])
print("object type:", object_row["object_type"])
```

## Usage
OpenHotels and its associated models are provided to support research and educational work on visual recognition, retrieval, representation learning, and related areas. Because visual datasets and models can be used in contexts beyond those anticipated by their creators, access to OpenHotels and its associated models is gated to support thoughtful, responsible use and to make these usage expectations visible to prospective users.

These resources should not be used:
- to identify, locate, track, profile, or target specific individuals;
- in ways that facilitate surveillance, harassment, discrimination, exploitation, or other harm;
- to infer sensitive personal information about individuals;
- in law-enforcement, immigration-enforcement, military, intelligence, or commercial-surveillance applications without first consulting the maintainers;
- to reconstruct, re-identify, or expose information that has been removed, obscured, or anonymized;
- to redistribute the dataset, model weights, or access credentials to individuals who have not separately received access; or
- to create or release derivative resources that materially increase the risk of misuse without appropriate ethical and safety review.

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.

Users are responsible for determining whether their proposed work requires review by an institutional review board, ethics committee, data-protection office, or other relevant authority. Access to these resources should not be understood as ethical, legal, institutional, or regulatory approval for a particular project.

We encourage users to describe the limitations and foreseeable risks of these resources in resulting publications and other outputs, consider the people and communities who could be affected by their use, and avoid claims or demonstrations that encourage harmful or inappropriate deployment.