This repository contains the Species dataset, the StreetHazards dataset, and some code for the paper Scaling Out-of-Distribution Detection for Real-World Settings.

Download the Species OOD detection dataset here.

Download the StreetHazards OOD segmentation dataset here.

The optional StreetHazards training set is available here. Also, the BDD-Anomaly dataset is sourced from the BDD100K dataset. Code for the multi-label out-of-distribution detection experiments is available in this repository.

git clone --recursive https://github.com/hendrycks/anomaly-seg

cd anomaly-seg
mv defaults.py semantic-segmentation-pytorch/config
mv anom_utils.py semantic-segmentation-pytorch/
mv dataset.py semantic-segmentation-pytorch/
mv eval_ood.py semantic-segmentation-pytorch/
mv create_dataset.py semantic-segmentation-pytorch/
cd semantic-segmentation-pytorch

# Place the above download in semantic-segmentation-pytorch/data/
cd data/
tar -xvf streethazards_train.tar
cd ..
python3 create_dataset.py

# Train pspnet or another model on our dataset
python3 train.py

# To evaluate the model on out of distribution test set
python3 eval_ood.py DATASET.list_val ./data/test.odgt

Note: to run on single gpu please refer to this issue#3.

To evaluate the model performance using a CRF with our code please install

pip install pydensecrf

The source package is from https://github.com/lucasb-eyer/pydensecrf

We cannot reshare the images from BDD100K so please visit BDD website to download them. The images should be from the 10K set of images that they released.

We have shared the labels in the folder called seg and part of the process by which we created these labels in create_bdd_dataset.py. To be able to fully utilize these labels one just needs to pattern match the label ids to the image id (they're the same) from our labels to the BDD images.

Pretrained models weights are availble at this Google drive link.

If you find this useful in your research, please consider citing:

@article{hendrycks2019anomalyseg,
  title={Scaling Out-of-Distribution Detection for Real-World Settings},
  author={Hendrycks, Dan and Basart, Steven and Mazeika, Mantas and Zou, Andy and Kwon, Joe and Mostajabi, Mohammadreza and Steinhardt, Jacob and Song, Dawn},
  journal={ICML},
  year={2022}
}