Update README.md

README.md

@@ -6,38 +6,44 @@ pretty_name: STAR
 size_categories:
 - 100M<n<1B
 ---
-# Dataset Card for Dataset Name
 
-<!-- Provide a quick summary of the dataset. -->
 
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
-
-
-### Dataset Description
-
-<!-- Provide a longer summary of what this dataset is. -->
-
-## Uses
-
-<!-- Address questions around how the dataset is intended to be used. -->
-
-[More Information Needed]
-
-## Dataset Structure
-
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-
-[More Information Needed]
-
-### Source Data
-
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-
-#### Data Collection and Processing
-
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+# 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`
+
+## Loading Sample
+```python
+from datasets import Dataset
+import numpy as np
+import glob
+
+# Load paired data
+train_hr_files = glob.glob("sampled_data/x2/train_hr_patch/*.npy")
+train_lr_files = [f.replace("hr_patch", "lr_patch") for f in train_hr_files]
+eval_hr_files = glob.glob("sampled_data/x2/eval_hr_patch/*.npy")
+eval_lr_files = [f.replace("hr_patch", "lr_patch") for f in eval_hr_files]
+data_dict = {
+    "hr_data": [np.load(f) for f in train_hr_files + eval_hr_files],
+    "lr_data": [np.load(f) for f in train_lr_files + eval_lr_files],
+    "split": ["train"] * len(train_hr_files) + ["eval"] * len(eval_hr_files)
+}
+dataset = Dataset.from_dict(data_dict)
+print(dataset[0]["hr_data"].shape)  # Example access