Skip to content

Latest commit

 

History

History
117 lines (94 loc) · 3.73 KB

README.md

File metadata and controls

117 lines (94 loc) · 3.73 KB
Raw

Is Synthetic Data All We Need? Benchmarking the Robustness of Models Trained with Synthetic Images

This is the official repository accompanying the CVPR Workshop paper:

Is Synthetic Data All We Need? Benchmarking the Robustness of Models Trained with Synthetic Images (CVPR Workshops 2024)

arXiv

Project Page

🔧 Dependencies

The repo depends upon the following

Python 3.8.5

PyTorch 2.2.1

CUDA 12.1

🚜 Environment

conda create -n bench-syn-clone python==3.8.5

conda activate bench-syn-clone

conda install pytorch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 pytorch-cuda=12.1 -c pytorch -c nvidia

pip install -r requirements.txt

💻 Model Files

The pretrained models files that we used in our paper can be downloaded from here.

🚀 Running evaluation on your models

To evaluate your own, put it in the pretrained_models/Pretrained_Models folder and edit the model.py file.

Please change the dataset_path and save_path in the config/default.yaml file for each evaluation metric.

Calibration

Please donwload the ImageNet-A and ImageNet-R datasets.

cd calibration 
bash scripts/run_multiple.sh
bash scripts/run_clip.sh

Background Bias

Please download the ImageNet-9 dataset.

cd background_bias
bash scripts/run_multiple.sh
bash scripts/run_clip.sh

Shape Bias

cd shape_bias
bash scripts/run_multiple.sh
bash scripts/run_clip.sh

Context Bias

Please download the FOCUS dataset and then run,

cd context_bias
bash scripts/run_mutliple.sh

OOD Detection

Download the iNaturalist, Places, and SUN datasets.

cd ood_detection
bash scripts/run_multiple.sh
bash scripts/run_clip.sh

2D corruptions

Download the 2D-corruptions dataset from https://zenodo.org/records/2235448

cd corruptions
bash scripts/run_multiple_2dcc.sh
bash scripts/run_clip_2dcc.sh

3D corruptions

Download the 3D-corruptions dataset from https://datasets.epfl.ch/3dcc/index.html

cd corruptions
bash scripts/run_multiple_3dcc.sh
bash scripts/run_clip_3dcc.sh

Adversarial Perturbations

We use the Ares package for running our attacks.

cd adversarial_attack/ares/classification
bash scripts/run_multiple_fgsm.sh
bash scripts/run_multiple_pgd.sh
bash scripts/run_clip_fgsm.sh
bash scripts/run_clip_pdg.sh

BibTex

@inproceedings{singh2024synthetic,
  title={Is Synthetic Data All We Need? Benchmarking the Robustness of Models Trained with Synthetic Images},
  author={Singh, Krishnakant and Navaratnam, Thanush and Holmer, Jannik and Schaub-Meyer, Simone and Roth, Stefan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2505--2515},
  year={2024}
}

License

License

Contact

Krishnakant Singh (firstname.lastname@visinf.tu-darmstadt.de)