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README.md

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+---
+license: mit
+task_categories:
+- image-to-image
+tags:
+- image-compression
+- image-restoration
+- computer-vision
+size_categories:
+- n<1K
+---
+
+# DF2K_OST 128×128
+
+## Dataset Description
+
+Preprocessed version of the DF2K dataset (DIV2K + Flickr2K) for training image autoencoders.
+
+- **Total images**: 900
+- **Image size**: 128×128 pixels
+- **Format**: PNG (RGB)
+- **Source**: DIV2K high-quality image dataset
+
+## Dataset Structure
+
+```
+df2k_ost_128/
+├── img_00000.png
+├── img_00001.png
+├── ...
+└── img_00899.png
+```
+
+## Usage
+
+```python
+from huggingface_hub import hf_hub_download
+import os
+
+# Download all images
+for i in range(900):
+    filename = f"img_{i:05d}.png"
+    hf_hub_download(
+        repo_id="gperdrizet/autoencoders",
+        filename=filename,
+        repo_type="dataset",
+        local_dir="./data"
+    )
+```
+
+Or use with the custom data loader:
+
+```python
+from src.data_utils import load_df2k_ost
+
+images = load_df2k_ost(image_size=128)
+```
+
+## Original Dataset
+
+- **DIV2K**: https://data.vision.ee.ethz.ch/cvl/DIV2K/
+- **License**: https://data.vision.ee.ethz.ch/cvl/DIV2K/
+
+## Preprocessing
+
+1. Downloaded DIV2K train + validation sets
+2. Resized to 128×128 using Lanczos resampling
+3. Saved as optimized PNG files
+
+## Citation
+
+If you use this dataset, please cite the original DIV2K paper:
+
+```bibtex
+@InProceedings{Agustsson_2017_CVPR_Workshops,
+    author = {Agustsson, Eirikur and Timofte, Radu},
+    title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study},
+    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
+    month = {July},
+    year = {2017}
+}
+```