Update README.md

README.md

@@ -1,19 +1,118 @@
-# Galaxy Zoo 2 Data
+# Galaxy Zoo 2 Dataset
 
-This directory contains the Galaxy Zoo 2 dataset used for training the hierarchical ViT model.
+This directory contains the dataset used for training and evaluating the **Hierarchical Probabilistic Vote Regression model** on Galaxy Zoo 2.
+
+---
+
+## Dataset Source
+
+The dataset is derived from **Galaxy Zoo 2 (GZ2)**, a large-scale citizen science project providing galaxy morphology annotations.
+
+* Total images: ~243,000
+* Labels: volunteer vote distributions across a hierarchical decision tree
+
+The data corresponds to:
+
+* Willett et al. (2013)
+* Hart et al. (2016)
+
+---
 
 ## Directory Structure
-- `raw/`: Raw files from Zenodo (images + mapping CSV).
-- `processed/`: Final curated dataset (dr7objid-named images + clean `labels.parquet`).
-
-## How to get the data
-1. Download the images from the [Zenodo GZ2 repository](https://zenodo.org/record/3565489).
-2. Place the images in `data/raw/images_zenodo/`.
-3. Put `gz2_hart16.csv` and `gz2_filename_mapping.csv` in `data/raw/`.
-4. Run the preparation pipeline:
-   ```bash
-   python3 scripts/data/convert_zenodo_images.py
-   python3 scripts/data/prepare_labels.py
-   ```
-
-Note: The datasets are large (~3GB for labels, ~20GB for images) and are excluded from version control via `.gitignore`.
+
+```text
+data/
+├── raw/         # Original data (Zenodo images + metadata CSVs)
+├── processed/   # Final curated dataset
+└── README.md
+```
+
+---
+
+## Raw Data
+
+The raw dataset includes:
+
+* Galaxy images from Zenodo (`images_gz2.zip`)
+* `gz2_hart16.csv` — morphological vote distributions
+* `gz2_filename_mapping.csv` — mapping between image filenames and object IDs
+
+Each galaxy is identified by a **DR7 object ID (`dr7objid`)**, which links images to labels.
+
+---
+
+## Processing Pipeline (This Work)
+
+The raw Galaxy Zoo 2 dataset is not directly suitable for training.
+
+In this project, the following preprocessing steps are performed:
+
+* Map images from `asset_id` → `dr7objid`
+* Rename images for consistent indexing
+* Align images with corresponding labels
+* Remove missing or unmatched samples
+* Filter invalid or incomplete entries
+* Construct hierarchical vote distributions
+* Ensure all probability vectors are valid (sum = 1)
+
+Pipeline implementation:
+
+```bash
+python3 scripts/data/convert_zenodo_images.py
+python3 scripts/data/prepare_labels.py
+```
+
+---
+
+## Processed Data
+
+The final dataset consists of:
+
+* Cleaned and aligned galaxy images
+* `labels.parquet` containing structured vote distributions
+
+This dataset is directly used for training probabilistic models.
+
+---
+
+## Notes
+
+* Final dataset size: **~3.3 GB (images + labels)**
+* A small fraction of samples may be missing due to incomplete mappings
+* Missing samples are assumed to be randomly distributed
+
+The processed dataset ensures:
+
+* consistent mapping between images and labels
+* valid probability distributions
+* suitability for hierarchical probabilistic modeling
+
+---
+
+## Usage
+
+This dataset is designed for:
+
+* probabilistic regression over Galaxy Zoo decision trees
+* predicting full vote distributions instead of single labels
+* training models that preserve uncertainty and distribution structure
+
+---
+
+## Citation
+
+If you use this dataset, please cite:
+
+* Willett, K. W., et al. (2013)
+  *Galaxy Zoo 2: detailed morphological classifications for galaxies*
+  https://arxiv.org/abs/1308.3496
+
+* Hart, R. E., et al. (2016)
+  *Galaxy Zoo: improved debiased morphological classifications*
+  https://arxiv.org/abs/1607.01019
+
+---
+
+## Summary
+
+This dataset represents a cleaned and structured version of Galaxy Zoo 2, enabling reliable training of models that predict **hierarchical probability distributions** rather than discrete labels.