metadata

license: mit
pretty_name: Churches
tags:
  - image
  - computer-vision
  - buildings
  - architecture
task_categories:
  - image-classification
language:
  - en
configs:
  - config_name: default
    data_files: train/**/*.arrow
    features:
      - name: image
        dtype: image
      - name: unique_id
        dtype: string
      - name: width
        dtype: int32
      - name: height
        dtype: int32
      - name: image_mode_on_disk
        dtype: string
      - name: original_file_format
        dtype: string

Churches

High resolution image subset from the Aesthetic-Train-V2 dataset, a collection of Church buildings including facades, interior shots and landscapes.

Dataset Details

Curator: Roscosmos
Version: 1.0.0
Total Images: 780
Average Image Size (on disk): ~5.8 MB compressed
Primary Content: Church buildings
Standardization: All images are standardized to RGB mode and saved at 95% quality for consistency.

Dataset Creation & Provenance

1. Original Master Dataset

This dataset is a subset derived from: zhang0jhon/Aesthetic-Train-V2

Link: https://huggingface.co/datasets/zhang0jhon/Aesthetic-Train-V2
Providence: Large-scale, high-resolution image dataset, refer to its original dataset card for full details.
Original License: MIT

2. Iterative Curation Methodology

CLIP retrieval / manual curation.

Dataset Structure & Content

This dataset offers the following configurations/subsets:

Default (Full train data) configuration: Contains the full, high-resolution image data and associated metadata. Each example (row) in the dataset contains the following fields:
image: The actual image data. In the default (full) configuration.
unique_id: A unique identifier assigned to each image.
width: The width of the image in pixels (from the full-resolution image).
height: The height of the image in pixels (from the full-resolution image).

Usage

To download and load this dataset from the Hugging Face Hub:


from datasets import load_dataset, Dataset, DatasetDict

# Login using e.g. `huggingface-cli login` to access this dataset

# To load the full, high-resolution dataset (recommended for training):
# This will load the 'default' configuration's 'train' split.
ds_main = load_dataset("ROSCOSMOS/Church_Buildings", "default")

print("Main Dataset (default config) loaded successfully!")
print(ds_main)
print(f"Type of loaded object: {type(ds_main)}")

if isinstance(ds_main, Dataset):
    print(f"Number of samples: {len(ds_main)}")
    print(f"Features: {ds_main.features}")
elif isinstance(ds_main, DatasetDict):
    print(f"Available splits: {list(ds_main.keys())}")
    for split_name, dataset_obj in ds_main.items():
        print(f"  Split '{split_name}': {len(dataset_obj)} samples")
        print(f"  Features of '{split_name}': {dataset_obj.features}")

Citation

@inproceedings{zhang2025diffusion4k,
    title={Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion Models},
    author={Zhang, Jinjin and Huang, Qiuyu and Liu, Junjie and Guo, Xiefan and Huang, Di},
    year={2025},
    booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
}
@misc{zhang2025ultrahighresolutionimagesynthesis,
    title={Ultra-High-Resolution Image Synthesis: Data, Method and Evaluation},
    author={Zhang, Jinjin and Huang, Qiuyu and Liu, Junjie and Guo, Xiefan and Huang, Di},
    year={2025},
    note={arXiv:2506.01331},
}

Disclaimer and Bias Considerations

Please consider any inherent biases from the original dataset and those potentially introduced by the automated filtering (e.g., CLIP's biases) and manual curation process.

Contact

N/A