README.md

metadata

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

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.

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.

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.

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.

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"])