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README.md

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 ---
 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

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