OOD Segmentation, Tracking and Retrieval:

For a detailed description, please refer to https://arxiv.org/abs/2210.02074.

Packages and their versions:

Code tested with Python 3.6.10 and pip 21.3.1. Install Python packages via

pip install -r requirements.txt

Download weights:

mkdir checkpoints
wget -O ./checkpoints/DeepLabV3+_WideResNet38_cityscapes.pth https://uni-wuppertal.sciebo.de/s/WVFTc4ka37xASZV/download
wget -O ./checkpoints/DeepLabV3+_WideResNet38_entropy_maximized.pth https://uni-wuppertal.sciebo.de/s/kCgnr0LQuTbrArA/download

Datasets:

• https://zenodo.org/communities/buw-ood-tracking
• https://uni-wuppertal.sciebo.de/s/RYiJpr3NGZ3PWER

Preparation:

Edit all necessary paths stored in "config.yaml". By default the outputs will be saved in "./outputs". Also, in the same file, select the tasks to be executed by setting the corresponding boolean variable (True/False). These functions are CPU based and parts are parallized over the number of input images, adjust "num_cpus" in "config.yaml" to make use of this.

Run the code:

python main.py

Code adapted from:

• https://github.com/SegmentMeIfYouCan/road-anomaly-benchmark
• https://github.com/mrottmann/MetaSeg
• https://github.com/robin-chan/meta-ood
• https://github.com/kmaag/Time-Dynamic-Prediction-Reliability
• https://github.com/RonMcKay/OODRetrieval

Authors:

• Kira Maag (Ruhr University Bochum)
• Robin Chan (Bielefeld University)
• Svenja Uhlemeyer (University of Wuppertal)
• Kamil Kowol (University of Wuppertal)