ForAug

This is the public code repository for the paper ForAug: Recombining Foregrounds and Backgrounds to Improve Vision Transformer Training with Bias Mitigation.

Updates

[24.03.2025] We have integrated ForNet into Huggingface Datasets for easy and convenient use 🤗 💫
[19.03.2025] We release the code to download and use ForNet in this GitHub repo 💻
[19.03.2025] We release the patch files of ForNet on Huggingface 🤗
[12.03.2025] We release the preprint of ForAug on arXiv 🗒️

Using ForAug/ForNet

With 🤗 Huggingface Datasets

We have integrated ForNet into 🤗 huggingface datasets:

from datasets import load_dataset

ds = load_dataset(
    "TNauen/ForNet",
    trust_remote_code=True,
    split="train",
)

⚠️ You must be authenticated and have access to the ILSVRC/imagenet-1k dataset on the hub, since it is used to apply the patches and get the foreground and background information.

⚠️ Be prepared to wait while the files are downloaded and the patches are applied. This will only happen the first time you load the dataset. By default, well use as many CPU cores as available on the system. To limit the number of cores used set the MAX_WORKERS environment variable.

You can pass additional parameters to control the recombination phase:

background_combination: Which backgrounds to combine with foregrounds. Options: "orig", "same", "all".
fg_scale_jitter: How much should the size of the foreground be changed (random ratio). Example: (0.1, 0.8).
pruning_ratio: For pruning backgrounds, with (foreground size/background size) $\geq$ . Backgrounds from images that contain very large foreground objects are mostly computer generated and therefore relatively unnatural. Full dataset: 1.1.
fg_size_mode: How to determine the size of the foreground, based on the foreground sizes of the foreground and background images. Options: "range", "min", "max", "mean".
fg_bates_n: Bates parameter for the distribution of the object position in the foreground. Uniform Distribution: 1. The higher the value, the more likely the object is in the center. For fg_bates_n = 0, the object is always in the center.
mask_smoothing_sigma: Sigma for the Gaussian blur of the mask edge.
rel_jut_out: How much is the foreground allowed to stand/jut out of the background (and then cut off).
orig_img_prob: Probability to use the original image, instead of the fg-bg recombinations. Options: 0.0-1.0, "linear", "revlinear", "cos".

For orig_img_prob schedules to work, you need to set ds.epochs to the total number of epochs you want to train. Before each epoch set ds.epoch to the current epoch ($0 \leq$ ds.epoch $<$ ds.epochs).

To recreate out evaluation metrics, you may set:

fg_in_nonant: Integer from 0 to 8. This will scale down the foreground and put it into the corresponding nonant (part of a 3x3 grid) in the image.
fg_size_fact: The foreground object is (additionally) scaled by this factor.

Local Installation

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 this 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.

4. Validate the ForNet files

To validate that you have all required files, run

python validate.py -f <data_path>

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) at data_path. Additionally, you need to install the (standard) requirements from 'requirements.txt':

pip install -r requirements.txt

Then, just do

from fornet import ForNet

data_path = ...

dataset = ForNet(
            data_path,
            train=True,
            transform=None,
            background_combination="all",
          )

For information on all possible parameters, run

from fornet import ForNet

help(ForNet.__init__)

Citation

@misc{nauen2025foraug,
      title={ForAug: Recombining Foregrounds and Backgrounds to Improve Vision Transformer Training with Bias Mitigation},
      author={Tobias Christian Nauen and Brian Moser and Federico Raue and Stanislav Frolov and Andreas Dengel},
      year={2025},
      eprint={2503.09399},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
}

ToDos

release code to download and create ForNet
release code to use ForNet for training and evaluation
integrate ForNet into Huggingface Datasets
release code for the segmentation phase