Code for paper entitled "Improving Novelty Detection using the Reconstructions of Nearest Neighbours"

Related tags

Deep Learning NLN
Overview

NLN: Nearest-Latent-Neighbours

A repository containing the implementation of the paper entitled Improving Novelty Detection using the Reconstructions of Nearest Neighbours

Installation

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

Replication of results in paper

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

MVTec-AD usage

You will need to download the MVTec anomaly detection dataset and specify the its path using -mvtec_path command line option.

Training

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> \

Reporting Results

Run the following given the correctly generated results files:

    python report.py -data <MNIST,CIFAR10,FASHION_MNIST,MVTEC> -seed <filepath-seed>

Licensing

Source code of NLN is licensed under the MIT License.

Comments
  • Refactor Code

    Refactor Code

    General

    Issue documenting post submission code refactoring.

    • [x] Merge branches on gpu-01 and gpu-02
    • [x] ~~Join together mvtec_metrics and nln_metrics so that the nearest neighbour search isn't done twice~~
      • Left separate as its easier to follow
    • [x] Add /reporting/segmentation.py to be automatically generated after training model
    • [x] Add /reporting/residuals.py to be automatically generated after training model
    • [x] Remove unused architectures from /architecture
    • [x] neval.py needs to be integrated into training pipeline for mvtec data
    • [x] Make cmd argument for MVTec data directory path
    opened by mesarcik 1
  • Models failing to converge

    Models failing to converge

    Description

    A number of models are failing to converge, this issue documents this problem.

    AE

    • [x] zipper

    VAE

    • [x] wood
    • [x] zipper

    AAE

    • [x] wood
    • [x] zipper

    AE-con

    • [x] carpet
    • [x] cable
    • [x] pill
    • [x] screw
    • [x] tile
    • [x] toothbrush
    • [x] wood
    • [x] zipper

    AE-res

    • [x] capsule (working version on gpu-01)
    • [x] hazelnut
    • [x] leather
    • [X] toothbrush
    • [x] wood
    • [x] zipper
    opened by mesarcik 0
  • Model_loader changes and comments added

    Model_loader changes and comments added

    • In model_loader.py the load_model() function was removed as it is not used and was written for an earlier version of the software
    • Docstrings added where necessary
    opened by mesarcik 0
  • AE error restored by procesing training data

    AE error restored by procesing training data

    • Added process() to data.py
    • Uses sklearn.MinMaxScaler on a per image basis to scale the images between 0 and 1
    • Seems to fix the bug with the AE error exploding
    opened by mesarcik 0
  • tf.abs was replaced with np.abs, sklearn dependancies updated

    tf.abs was replaced with np.abs, sklearn dependancies updated

    • tf.abs was resulting in tf.nans so it was replaced with np
    • sklearn doesn't automatically install 0.23.2 so updated it in the dependancies.
    • It all seems to be working now on das5
    opened by mesarcik 0
Owner
Michael (Misha) Mesarcik
Electrical and Computer Engineer
Michael (Misha) Mesarcik
GitHub https://arxiv.org/abs/2111.06150
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