Update README.md

README.md

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----
-license: cc-by-4.0
----
+---
+license: odc-by
+task_categories:
+- image-classification
+- image-to-image
+language:
+- en
+tags:
+- astronomy
+- galaxy-morphology
+- computer-vision
+- galaxy-zoo
+- sdss
+pretty_name: Galaxy Zoo 2 Preprocessed
+size_categories:
+- 10K<n<100K
+- 100K<n<1M
+---
+
+# Galaxy Zoo 2 - Preprocessed Dataset
+
+Preprocessed galaxy images from SDSS with Galaxy Zoo 2 morphological classifications, ready for deep learning.
+
+
+## 📁 Dataset Structure
+
+```
+    ├── labels.csv                 # All ~239K galaxy labels
+    └── preprocessed_69x69/        # Preprocessed images
+
+    ├── labels_sampled.csv         # 25K stratified sample
+    ├── preprocessed_69x69/        # Preprocessed images
+```
+
+## 🖼️ Image Format
+
+- **Size**: 69×69 pixels RGB
+- **Format**: Compressed NumPy arrays (.npz)
+- **Shape**: (69, 69, 3)
+- **Type**: float32, range [0, 1]
+- **Source**: SDSS DR19
+
+## 🏷️ Labels
+
+37 morphological probability distributions from Galaxy Zoo 2:
+- Smooth vs. Featured
+- Spiral arms
+- Edge-on orientation  
+- Bars, bulges, mergers
+- And more...
+
+All labels are **debiased** probabilities accounting for volunteer expertise and question tree structure.
+
+
+## 🔬 Preprocessing Pipeline
+
+1. Download 424×424 SDSS cutouts
+2. Center crop to 207×207
+3. Downscale to 69×69 (Lanczos)
+4. Normalize to [0, 1]
+5. Save as compressed .npz
+
+Based on [Dieleman et al. (2015)](https://arxiv.org/abs/1503.07077)
+
+## 📈 Sampled Dataset Details
+
+**Stratified Sampling Strategy:**
+- Maintains morphological diversity
+- Minimum 2,000 images per category
+- Categories: Smooth, Spiral, Edge-on, Featured, Artifact, Uncertain
+
+See `sampled/sampling_summary.txt` for complete statistics.
+
+## 🎓 Citation
+
+```bibtex
+@article{willett2013galaxy,
+  title={Galaxy Zoo 2: detailed morphological classifications for 304,122 galaxies from the Sloan Digital Sky Survey},
+  author={Willett, Kyle W and others},
+  journal={Monthly Notices of the Royal Astronomical Society},
+  volume={435},
+  number={4},
+  pages={2835--2860},
+  year={2013}
+}
+```
+
+## 📜 License
+
+- **Images**: Public domain (SDSS)
+- **Labels**: ODC-By (Galaxy Zoo 2)
+
+## 🔗 Links
+
+- [Galaxy Zoo Project](https://www.galaxyzoo.org/)
+- [SDSS](https://www.sdss.org/)
+- [Original Paper](https://academic.oup.com/mnras/article/435/4/2835/1749111)
+- [Preprocessing Method](https://arxiv.org/abs/1503.07077)
+
+## ❓ FAQ
+
+**Q: Which version should I download?**  
+A: Start with **sampled/** (25K images) for quick experiments. Use **full/** only if you need maximum performance and have 40+ hours for training.
+
+**Q: What format are the images?**  
+A: Compressed NumPy arrays (.npz). Load with: `np.load('file.npz')['image']`
+
+**Q: Are images augmented?**  
+A: No. Apply rotation/flipping/scaling during training for best results.
+
+**Q: Why 69×69 resolution?**  
+A: Follows Dieleman et al. (2015) methodology - sufficient for morphology while enabling efficient training.
+
+
+
+## Credits
+
+**Original Data:** Galaxy Zoo 2 Team, SDSS Collaboration, 300,000+ volunteers
+
+---