---
license: mit
language:
- en
pretty_name: STAR
size_categories:
- 100M<n<1B
task_categories:
- image-to-image
tags:
- astronomy
- super-resolution
- computer-vision
configs:
- config_name: default
  data_files:
  - split: train
    path: sampled_data/x2/train_metadata.jsonl
  - split: validation
    path: sampled_data/x2/validation_metadata.jsonl
---


# STAR Dataset
The **STAR (Super-Resolution for Astronomical Star Fields)** dataset is a large-scale benchmark for developing field-level super-resolution models in astronomy. It contains **54,738 flux-consistent image pairs** derived from Hubble Space Telescope (HST) high-resolution observations and physically faithful low-resolution counterparts. The dataset addresses three key challenges in astronomical super-resolution:

- **Flux Inconsistency**: Ensures consistent flux using a flux-preserving data generation pipeline.
- **Object-Crop Configuration**: Strategically samples patches across diverse celestial regions.
- **Data Diversity**: Covers dense star clusters, sparse galactic fields, and regions with varying background noise.

The dataset includes x2 and x4 scaling pairs in `.npy` format, suitable for training and evaluating super-resolution models.

## Structure
- **Full Data**:
  - `data/x2/x2.tar.gz` (33GB): Full x2 dataset.
  - `data/x4/x4.tar.gz` (29GB): Full x4 dataset.
  - Unzip to access: `train_hr_patch/`, `train_lr_patch/`, `eval_hr_patch/`, `eval_lr_patch/` (.npy files), and `dataload_filename/` (txt with pairs).

- **Sample Data** (for testing and Croissant, x2 only):
  - `sampled_data/x2/`: Sample .npy pairs (500 train pairs, 100 eval pairs).
    - `train_hr_patch/`, `train_lr_patch/` (500 files each)
    - `eval_hr_patch/`, `eval_lr_patch/` (100 files each)
  - Croissant metadata: `sampled_data/x2/croissant.json`


## Custom Loading with Split
To load with split="train":
```python
from datasets import load_dataset
data_files = {
    "train": ["sampled/x2/train_hr_patch/*.npy", "sampled/x2/train_lr_patch/*.npy"],
    "validation": ["sampled/x2/eval_hr_patch/*.npy", "sampled/x2/eval_lr_patch/*.npy"]
}
dataset = load_dataset("KUOCHENG/STAR", data_files=data_files, split="train")