A repository containing the implementation of the paper entitled Improving Novelty Detection using the Reconstructions of Nearest Neighbours
Install conda environment by:
conda create --name nln python=3.7
Run conda environment by:
conda activate nln
Install dependancies by running:
pip install -r dependancies
Additionally for training on a GPU run:
conda install -c anaconda tensorflow-gpu=2.2.0
Run the following to replicate the results for MNIST, CIFAR-10, Fashion-MNIST and MVTec-AD respectively
sh experiments/run_mnist.sh
sh experiments/run_cifar.sh
sh experiments/run_fmnist.sh
sh experiments/run_mvtec.sh
Or to execute all experiments sequentially the following script can be run:
sh experiments/run_all.sh
You will need to download the MVTec anomaly detection dataset and specify the its path using -mvtec_path command line option.
Run the following:
python main.py -anomaly_class <0,1,2,3,4,5,6,7,8,9,bottle,cable,...> \
-percentage_anomaly <float> \
-limit <int> \
-epochs <int> \
-latent_dim <int> \
-data <MNIST,FASHION_MNIST,CIFAR10,MVTEC> \
-mvtec_path <str>\
-neighbors <int(s)> \
-algorithm <knn> \
-patches <True, False> \
-crop <True, False> \
-rotate <True, False> \
-patch_x <int> \
-patch_y <int> \
-patch_x_stride <int> \
-patch_y_stride <int> \
-crop_x <int> \
-crop_y <int> \
Run the following given the correctly generated results files:
python report.py -data <MNIST,CIFAR10,FASHION_MNIST,MVTEC> -seed <filepath-seed>
Source code of NLN is licensed under the MIT License.