A PyTorch implementation of "Anomaly Detection using Deep Learning based Image Completion". This paper was published at the 17th IEEE International Conference on Machine Learning and Applications (ICMLA). You can find it on arXiv.
First step is to install the dependencies. On my machine I have used a conda environment, but the project can be run with venv or without any environment at all.
# Use only one option from below
# Install packages within a Conda environment
$ conda create -n deep-ad -f environment.yml
# Install packages within a virtual environment
$ pip install virtualenv
$ virtualenv deep-ad
$ source deep-ad/bin/activate
(deep-ad) $ pip install -r requirements.txt
# Install packages globally (not recommended)
$ pip install -r requirements.txt
In order to be able to use modules from src/deep_ad
inside notebooks we need to install the project. For development
purposes use --editable/-e
.
python -m pip install .
# OR
python -m pip install -e .
Required packages were added to environment.yml
and requirements.txt
files with these commands:
conda env export --from-history | findstr /v "^prefix" > environment.yml
pip list --format=freeze > requirements.txt
The findstr
function is a Windows equivalent for grep
, so on Linux use this instead:
conda env export --from-history | grep -v "^prefix" > environment.yml
The minimum required keys inside the .env
are the following:
running_env=HOME
# In this directory you should have at least the raw DAGM dataset
dagm_dir="C:\\path-to-datasets-directory"