Open Set Recognition using Vision Transformer

This repository provides the implementation of an open set recognition method using vision transformer.

Citation

If you use our method, please cite our paper.

Open Set Recognition using Vision Transformer with an Additional Detection Head
Feiyang Cai, Zhenkai Zhang, Jie Liu, and Xenofon Koutsoukos [PDF]

@article{cai2022open,
       author = {Cai, Feiyang and Zhang, Zhenkai and Liu, Jie and Koutsoukos, Xenofon},
        title = {Open Set Recognition using Vision Transformer with an Additional Detection Head},
      journal = {arXiv preprint arXiv:2203.08441},
         year = 2022,
}

(back to top)

Getting Started

This code repository includes the detailed instructions for reproducing experiments of MNIST, SVHN, CIFAR10, CIFAR+N, TinyImageNet, and CUB reported in the paper.

This repository is still updating. We will provide our trained models in this repository later.

All our experiments are performed in an 80-core Ubuntu Linux virtual machine with 128GB RAM and 4 Tesla V100 GPUs.

Prerequisites

Create an conda environment and install the dependencies

conda env create -f environment.yml

Activate the environment

conda activate osr

Pretrained ViT model

Download pretrained ViT-B/16 from this [link] and put it in the folder of "./pretrained_model/"

Datasets

Download the TinyImageNet dataset from this [link] and extract it in the folder of "./data/"

The other datasets will be downloaded automatically if they are not existed in the "./data/" folder.

(back to top)

Train

"scripts.txt" contains the complete scripts of training and evaluating for each experiment.

Stage 1 (Closed set training)

Train only 1 random "known/unknown" split trial

python train_classifier.py --exp-name osrclassifier --n-gpu 4 --tensorboard --image-size 224 --batch-size 256 --num-workers 16 --train-steps 4590 --lr 0.01 --wd 1e-5 --dataset <known_dataset> --num-classes <num_of_known_classes> --random-seed <random_seed>  --checkpoint-path ./pretrained_model/imagenet21k+imagenet2012_ViT-B_16.pth

Train 5 random "known/unknown" split trials

python ./train_classifier_batch.py --num-classes <num_of_known_classes> --checkpoint-path ./pretrained_model/imagenet21k+imagenet2012_ViT-B_16.pth --dataset <known_dataset>

where <known_dataset> can be MNIST, SVHN, CIFAR10, TinyImageNet, or CUB.

Stage 2 (Open set training)

Train the detector using the trained model from training stage 1

python train_detector.py --exp-name osrdetector --n-gpu 4 --tensorboard --image-size 224 --batch-size 256 --num-workers 16 --train-steps 4590 --lr 0.01 --wd 1e-5 --dataset <known_dataset> --num-classes <num_of_known_classes> --checkpoint-path <trained_model> --random-seed <random_seed>

Train detectors by loading all the models whose settings match <num_of_known_classes> and <known_dataset> in "./experiments/" folder

python ./train_detector_batch.py --num-classes <num_of_known_classes> --dataset <known_dataset>

(back to top)

Evaluation

Evaluate the trained model

python ./measure_osrdetector.py --exp-name osrdetector --in-dataset <known_dataset> --out-dataset <unknown_dataset> --in-num-classes <num_of_known_classes> --out-num-classes <num_of_unknown_classes>

(back to top)