---
language:
- en
license: unknown
task_categories:
- image-classification
pretty_name: DiffraNet
size_categories:
- 10K<n<100K
---

# DiffraNet

## Dataset description

This repository contains the **DiffraNet** diffraction image dataset introduced by Souza *et al.* in the paper **"DeepFreak: Learning Crystallography Diffraction Patterns with Automated Machine Learning"**. The original dataset consists of **512×512 grayscale diffraction images**, including both synthetic and real experimental data. The synthetic images were generated using the **nanoBragg** simulator, while the real images were collected from serial crystallography experiments. :contentReference[oaicite:0]{index=0}

This repository mirrors the original dataset and additionally includes a modified version of the raw real dataset (`real_raw_mod`) for my own purposes.

## Credit

The original DiffraNet dataset was created by:

- Artur Souza
- Leonardo B. Oliveira
- Sabine Hollatz
- Matt Feldman
- Kunle Olukotun
- James M. Holton
- Aina E. Cohen
- Luigi Nardi

### Paper

> Souza, A. *et al.* **DeepFreak: Learning Crystallography Diffraction Patterns with Automated Machine Learning** (2019)

https://arxiv.org/abs/1904.11834

### Original project

https://arturluis.github.io/diffranet/

All credit for the original dataset belongs to the authors. This repository is intended only as a mirror of the original dataset and includes one additional modified split (`real_raw_mod`) described below.

---

# Dataset structure

The repository contains four top-level directories:

```text
synthetic/
real_raw/
real_preprocessed/
real_raw_mod/
```

Each of the directories represents a different dataset or a different dataset version. 

The original **DiffraNet** dataset consisted of ```synthetic```, ```real_raw```, ```real_preprocessed```. 

```real_raw_mod``` is a modified version of ```real_raw``` for use in transfer learning, and was not a part of the original dataset. 

The folder hierarchy defines both the **dataset split** and the **class labels**.

## synthetic/

Synthetic diffraction images generated with the **nanoBragg** simulator.

Dataset splits:

- `training`
- `validation`
- `test`

Classes:

1. blank
2. no crystal
3. weak diffraction
4. good diffraction
5. strong diffraction

The original dataset contains approximately **25,000 synthetic images**.

## real_raw/

Original real diffraction images provided by the DiffraNet authors.

Characteristics:

- cropped to **512×512**
- contain the experimental beamstop shadow

Dataset splits:

- `validation`
- `test`

Classes:

1. no diffraction
2. diffraction

## real_preprocessed/

Original preprocessed version of the real dataset.

The images are identical to `real_raw` except that pixel intensities have been rescaled so that the mean pixel value matches that of the synthetic dataset.

The dataset organization is identical to `real_raw`.

## real_raw_mod/

Modified version of `real_raw` prepared for supervised learning.

The original `validation` split has been subdivided into

- `training`
- `validation`

while the original `test` split has been left unchanged.


---

# Dataset statistics

| Dataset | Images | Classes |
|---------|-------:|--------:|
| Synthetic | ~25,000 | 5 |
| Real (raw) | 457 | 2 |
| Real (preprocessed) | 457 | 2 |

All images are **512×512 grayscale**.

---

# Citation

If you use this dataset, please cite the original paper:

```bibtex
@article{souza2019deepfreak,
  title={DeepFreak: Learning Crystallography Diffraction Patterns with Automated Machine Learning},
  author={Souza, Artur and Oliveira, Leonardo B. and Hollatz, Sabine and Feldman, Matt and Olukotun, Kunle and Holton, James M. and Cohen, Aina E. and Nardi, Luigi},
  journal={arXiv preprint arXiv:1904.11834},
  year={2019}
}
```