Official PyTorch implementation of "Spatial Location Constraint Prototype Loss for Open Set Recognition".

More our open set recognition article and code information can be seen in https://arxiv.org/abs/2108.04225.

These codes are supposed to be run with a Linux system. If you use Windows system to run them, it may encounter some errors.

Currently, requires following packages

• python 3.6+
• torch 1.4+
• torchvision 0.5+
• CUDA 10.1+
• scikit-learn 0.22+

For Tiny-ImageNet, please download the following datasets to ./data/tiny_imagenet and unzip it.

• tiny_imagenet

To train open set recognition models in paper, run this command:

python osr.py --dataset <DATASET> --loss <LOSS>

Option

--loss can be one of SLCPLoss/GCPLoss/Softmax.

--dataset is one of mnist/svhn/cifar10/cifar100/tiny_imagenet.

Before getting the figure above, you need to train the LeNet++ network, whose architecture is in "./models/model.py".

• If you find our work or the code useful, please consider cite our paper using:

@misc{xia2021spatial,
      title={Spatial Location Constraint Prototype Loss for Open Set Recognition}, 
      author={Ziheng Xia and Ganggang Dong and Penghui Wang and Hongwei Liu},
      year={2021},
      eprint={2110.11013},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}