README: document pretraining/ + benchmark/ split with per-part download

README.md

@@ -13,6 +13,11 @@ tags:
 pretty_name: Moonstone
 size_categories:
 - 10K<n<100K
+configs:
+- config_name: benchmark
+  data_files: benchmark/*
+- config_name: pretraining
+  data_files: pretraining/mmap/*
 ---
 
 # Moonstone: A Multimodal Foundation Model Benchmark for Lunar Remote Sensing
@@ -23,14 +28,42 @@ GRAIL, Lunar Prospector GRS, Clementine). All channels are aligned to a common e
 grid (46,080 x 23,040 px, lunar sphere a=b=1,737,400 m) and organized into 7 physical modality
 groups (surface, thermal, spectral_M3, gravity, radar, hapke, composition).
 
-## Contents
+## Repository layout
+
+The dataset is split into two independent parts so you can download only what you need.
+
+### `pretraining/` — self-supervised pretraining data
 
 | Path | Description |
 |------|-------------|
-| `aligned/` | 28 source-of-truth GeoTIFFs at 128 ppd (+ M3 geometry, crater_mask) |
-| `mmap/` | Pre-normalized (z-scored) memory-mapped float32 arrays for pretraining (unlimited random crops) + NaN masks + channel_index.json |
-| `lunar_patches_v4.h5` | 16,200 patches (180x90 grid) x 28 x 256 x 256, with geology/age/mare metadata and fixed 70/15/15 split, for downstream evaluation |
-| `channel_stats.json` | Per-channel (mean, std) normalization statistics (200 random 256x256 windows) |
+| `pretraining/aligned/` | 28 source-of-truth instrument GeoTIFFs at 128 ppd (+ M3 geometry) |
+| `pretraining/mmap/` | Pre-normalized (z-scored) memory-mapped float32 arrays for pretraining (unlimited random crops) + NaN masks + `channel_index.json` |
+| `pretraining/channel_stats.json` | Per-channel (mean, std) normalization statistics (200 random 256x256 windows) |
+
+### `benchmark/` — downstream evaluation data
+
+| Path | Description |
+|------|-------------|
+| `benchmark/lunar_patches_v4.h5` | 16,200 patches (180x90 grid) x 28 x 256 x 256, with geology/age/mare metadata and fixed 70/15/15 split |
+| `benchmark/geologic_units.tif`, `benchmark/geologic_units.json` | USGS geologic-unit label raster + class map (geology + age tasks) |
+| `benchmark/mare_mask.tif` | Mare vs. highlands ground truth (segmentation) |
+| `benchmark/crater_mask.tif` | Crater (over 10 km) ground truth (segmentation) |
+
+## Downloading
+
+Download just one part (each is independent):
+
+```python
+from huggingface_hub import snapshot_download
+
+# Benchmark only (evaluation data + labels, ~74 GB)
+snapshot_download("ayushprd/Moonstone", repo_type="dataset",
+                  allow_patterns="benchmark/*", local_dir="Moonstone")
+
+# Pretraining only (z-scored arrays + source GeoTIFFs, ~207 GB)
+snapshot_download("ayushprd/Moonstone", repo_type="dataset",
+                  allow_patterns="pretraining/*", local_dir="Moonstone")
+```
 
 ## Channels (28)
 
@@ -47,6 +80,11 @@ Six downstream tasks define the benchmark.
 Geology (49-class), Age (5-class), Composition (FeO and TiO2 regression), Cross-modal thermal
 prediction, Mare and highlands segmentation, Crater (over 10 km) segmentation.
 
+## Code
+
+Pretraining and benchmark code (MG-MAE model, the 15-step data pipeline, and downstream
+evaluation for all six tasks): <https://github.com/ayushprd/Moonstone>
+
 ## Provenance
 All data derived from public NASA PDS / USGS / ODE archives. Built via the 15-step pipeline in
 the Moonstone code repository (steps 01-15 + fix_minirf). Normalization: z-score, NaN->0 after norm.