Update README.md

README.md

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+[![arXiv](https://img.shields.io/badge/arXiv-2503.09399-b31b1b?logo=arxiv)](https://arxiv.org/abs/2503.09399)
+[![Static Badge](https://img.shields.io/badge/GitHub-Repo-blue?logo=github)](https://github.com/tobna/ForAug)
+
 # ForAug/ForNet
+![ForAug](images/foraug.png)
 
 This is the ForNet dataset from the paper [ForAug: Recombining Foregrounds and Backgrounds to Improve Vision Transformer Training with Bias Mitigation](https://www.arxiv.org/abs/2503.09399).
 
-## Dataset Details
-
-### Dataset Description
-
-<!-- Provide a longer summary of what this dataset is. -->
-
-
-
-- **Curated by:** [Tobias Christian Nauen](https://tobias.nauen-it.de)
-
-### Dataset Sources [optional]
-
-<!-- Provide the basic links for the dataset. -->
-
-- **Repository:** [GitHub](https://github.com/tobna/ForAug)
-- **Paper:** [arXiv](https://www.arxiv.org/abs/2503.09399)
-
-## Usage
-Coming Soon
-
-
-## Dataset Structure
-
+### Updates
+
+- [19.03.2025] We release the code to download and use [ForNet on GitHub](https://github.com/tobna/ForAug) :computer:
+- [19.03.2025] We release the patch files of ForNet on Huggingface :hugs:
+- [12.03.2025] We release the preprint of [ForAug on arXiv](https://www.arxiv.org/abs/2503.09399) :spiral_notepad:
+
+
+## Using ForAug/ForNet
+
+### Preliminaries
+To be able to download ForNet, you will need the ImageNet dataset in the usual format at `<in_path>`:
+```
+<in_path>
+|--- train
+|    |--- n01440764
+|    |    |--- n01440764_10026.JPEG
+|    |    |--- n01440764_10027.JPEG
+|    |    |--- n01440764_10029.JPEG
+|    |    `-  ...
+|    |--- n01693334
+|    `-  ...
+`-- val
+     |--- n01440764
+     |    |--- ILSVRC2012_val_00000293.JPEG
+     |    |--- ILSVRC2012_val_00002138.JPEG
+     |    |--- ILSVRC2012_val_00003014.JPEG
+     |    `-  ...
+     |--- n01693334
+     `-  ...
+```
+
+### Downloading ForNet
+To download and prepare the already-segmented ForNet dataset at `<data_path>`, follow these steps:
+#### 1. Clone the git repository and install the requirements
+```
+git clone https://github.com/tobna/ForAug
+cd ForAug
+pip install -r prep-requirements.txt
+```
+
+#### 2. Download the diff files
+```
+./download_diff_files.sh <data_path>
+```
+This script will download all dataset files to `<data_path>`
+
+#### 3. Apply the diffs to ImageNet
+```
+python apply_patch.py -p <data_path> -in <in_path> -o <data_path>
+```
+This will apply the diffs to ImageNet and store the results in the `<data_path>` folder. It will also delete the already-processes patch files (the ones downloaded in step 2). In order to keep the patch files, add the `--keep` flag.
+
+#### Optional: Zip the files without compression
+When dealing with a large cluster and dataset files that have to be sent over the network (i.e. the dataset is on another server than the one used for processing) it's sometimes useful to not deal with many small files and have fewer large ones instead.
+If you want this, you can zip up the files (without compression) by using
+```
+./zip_up.sh <data_path>
+```
+
+### Creating ForNet from Scratch
+Coming soon
+
+### Using ForNet
+To use ForAug/ForNet you need to have it available in folder or zip form (see [Downloading ForNet](#downloading-fornet)) at `data_path`.
+Additionally, you need to install the (standard) requirements from 'requirements.txt':
+```
+pip install -r requirements.txt
+```
+
+Then, just do
+```python
+from fornet import ForNet
+
+data_path = ...
+
+dataset = ForNet(
+            data_path, 
+            train=True, 
+            transform=None, 
+            background_combination="all",
+          )
+
+```
+
+For information on all possible parameters, run
+```python
+from fornet import ForNet
+
+help(ForNet.__init__)
+```
+
+## Citation 
 Coming Soon
 
-## Dataset Creation
-
-Coming Soon
-## Citation [optional]
+### Dataset Sources
+- **Repository:**: [GitHub](https://github.com/tobna/ForAug)
+- **Paper:**: [arXiv](https://www.arxiv.org/abs/2503.09399)
+- **Project Page**: coming soon
 
-Coming Soon
-**BibTeX:**
+## ToDos
 
-[More Information Needed]
+- [x] release code to download and create ForNet
+- [x] release code to use ForNet for training and evaluation
+- [ ] integrate ForNet into Huggingface Datasets