README.md

---
license: cc-by-4.0
pretty_name: Raw space-based images from the Hubble Space Telescope 
tags:
- astronomy
- compression
- images
---

# SBI-16-2D Dataset

SBI-16-2D is a dataset which is part of the AstroCompress project. It contains imaging data assembled from the Hubble Space Telescope (HST). <TODO>Describe data format</TODO>

# Usage

You first need to install the `datasets` and `astropy` packages:

```bash
pip install datasets astropy
```

There are two datasets: `tiny` and `full`, each with `train` and `test` splits. The `tiny` dataset has 2 2D images in the `train` and 1 in the `test`. The `full` dataset contains all the images in the `data/` directory.

## Local Use (RECOMMENDED)

You can clone this repo and use directly without connecting to hf:

```bash
git clone https://huggingface.co/datasets/AstroCompress/SBI-16-2D
```

To pull all data files:
```
git lfs pull
```

Then `cd SBI-16-3D` and start python like:

```python
from datasets import load_dataset
dataset = load_dataset("./SBI-16-2D.py", "tiny", data_dir="./data/", writer_batch_size=1, trust_remote_code=True)
ds = dataset.with_format("np")
```

Now you should be able to use the `ds` variable like:

```python
ds["test"][0]["image"].shape # -> (TBD)
```

Note of course that it will take a long time to download and convert the images in the local cache for the `full` dataset. Afterward, the usage should be quick as the files are memory-mapped from disk.

## Use from Huggingface Directly

To directly use from this data from Huggingface, you'll want to log in on the command line before starting python:

```bash
huggingface-cli login
```

or

```
import huggingface_hub
huggingface_hub.login(token=token)
```

Then in your python script:

```python
from datasets import load_dataset
dataset = load_dataset("AstroCompress/SBI-16-2D", "tiny", writer_batch_size=1, trust_remote_code=True)
ds = dataset.with_format("np")
```