README.md

---
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
  - config_name: preview
    data_files: "preview/**/*.arrow"
    features:
      - name: image
        dtype: image
      - name: unique_id
        dtype: string
      - name: width
        dtype: int32
      - name: height
        dtype: int32
      - name: original_file_format
        dtype: string
      - name: image_mode_on_disk
        dtype: string
---

# Churches

High resolution image subset from the Aesthetic-Train-V2 dataset, a collection of 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.

### Who are the source data producers?
* Original Dataset Creators: Refer to the original dataset card.
* Curator and Refiner: Roscosmos

## 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. This is the recommended configuration for model training and full data analysis. The default split for this configuration is `train`.
* **`preview` configuration:** Contains a small, random subset of images from the `train` data. The images in this configuration are downsampled and re-compressed to be **viewer-compatible** on the Hugging Face Hub. The default split for this configuration is `train` (if no other split is specified).
Each example (row) in the dataset contains the following fields:

* `image`: The actual image data. In the default (full) configuration, this is full-resolution. In the preview configuration, this is a viewer-compatible version.
* `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:

```python

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/Churches", "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}")

# To load the smaller, viewer-compatible preview data (if available):
# This will load the 'preview' configuration's default split (often also 'train').
# Check your dataset card for exact config and split names.
# try:
#     ds_preview = load_dataset("{push_to_hub_id}", "preview") 
#     print("\nPreview Dataset (preview config):")
#     print(ds_preview)
#     print(f"Number of samples in the preview dataset: {len(ds_preview) if isinstance(ds_preview, Dataset) else 'N/A'}") 
# except ValueError as e:
#     print(f"\nPreview config not found or failed to load: {e}") 

# To access specific splits from a DatasetDict:
# my_train_data = ds_main['train']
# my_preview_data = ds_preview['train'] # if preview loads as DatasetDict

# The 'image' column will contain PIL Image objects.

```

## Citation

```bibtex
@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