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README.md

@@ -1,27 +1,102 @@
----
-license: mit
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: source
-    dtype: string
-  - name: filename
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 3365014045
-    num_examples: 26796
-  - name: validation
-    num_bytes: 374344385
-    num_examples: 2978
-  download_size: 3739083232
-  dataset_size: 3739358430
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
----
+---
+license: apache-2.0
+task_categories:
+- image-to-image
+- image-classification
+size_categories:
+- 10K<n<100K
+tags:
+- computer-vision
+- image-processing
+- autoencoders
+- image-compression
+- denoising
+---
+
+# DF2K_OST Dataset
+
+High-quality 256×256 image dataset for training autoencoders.
+
+## Dataset Description
+
+This dataset combines three high-quality image sources commonly used for image super-resolution and restoration tasks:
+
+- **DIV2K**: 900 high-resolution images (800 train + 100 validation)
+- **Flickr2K**: 2,650 high-resolution images
+- **OST (Outdoor Scene Training)**: 10,324 outdoor scene images
+
+All images have been resized to 256×256 pixels using Lanczos resampling for optimal quality.
+
+## Dataset Structure
+
+```
+DF2K_OST/
+├── train/          # ~90% of images (26796 images)
+└── validation/     # ~10% of images (2978 images)
+```
+
+Each sample contains:
+- `image`: 256×256 RGB image
+- `source`: Original dataset source (DIV2K, Flickr2K, or OST)
+- `filename`: Original filename
+
+## Processing
+
+All images were processed using:
+- Target resolution: 256×256 pixels
+- Resampling method: Lanczos (PIL.Image.LANCZOS)
+- Color mode: RGB
+- Train/validation split: 90/10 (stratified random)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load full dataset
+dataset = load_dataset("gperdrizet/DF2K_OST")
+
+# Load only training split
+train_data = load_dataset("gperdrizet/DF2K_OST", split="train")
+
+# Access images
+for sample in train_data:
+    image = sample['image']  # PIL Image
+    source = sample['source']  # Dataset source
+```
+
+## Original Sources
+
+### DIV2K
+- **Citation**: Agustsson, E., & Timofte, R. (2017). NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study.
+- **License**: See DIV2K dataset terms
+
+### Flickr2K
+- **Source**: Flickr images collected for super-resolution research
+- **License**: See original Flickr2K terms
+
+### OST (Outdoor Scene Training)
+- **Source**: Outdoor scene images for image restoration
+- **License**: See OST dataset terms
+
+## License
+
+The compilation and processing are provided under Apache 2.0 license. Individual images retain their original licenses from source datasets.
+
+## Citation
+
+If you use this dataset, please cite the original source datasets:
+
+```bibtex
+@inproceedings{agustsson2017ntire,
+  title={NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study},
+  author={Agustsson, Eirikur and Timofte, Radu},
+  booktitle={CVPR Workshops},
+  year={2017}
+}
+```
+
+## Created By
+
+Processed and compiled for the Autoencoders educational demo project.
+Repository: https://github.com/gperdrizet/autoencoders