Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
10K - 100K
ArXiv:
License:
File size: 3,787 Bytes
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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}
}
``` |