Datasets:

ROSCOSMOS
/

Church_Buildings

@@ -1,151 +1,115 @@
----
-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

+---
+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 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. This is the recommended configuration for model training and full data analysis. The default split for this configuration is `train`.
+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:
+```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}")
+```
+## 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