marwa000000000/SDSS-GalaxyZoo2-Preprocessed

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)

📈 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

@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
• SDSS
• Original Paper
• Preprocessing Method

❓ 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

---