Update README.md

README.md

@@ -1,3 +1,150 @@
 ---
+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}
+}
+```