This repository contains Pytorch code to replicate experiments in the CVPR19 paper "Latent Space Autoregression for Novelty Detection".
Please cite with the following BibTeX:
@inproceedings{abati2019latent,
title={{Latent Space Autoregression for Novelty Detection}},
author={Abati, Davide and Porrello, Angelo and Calderara, Simone and Cucchiara, Rita},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision and Pattern Recognition},
year={2019}
}
Specifically, performs:
First things first, clone this repository locally via git.
git clone https://github.com/cvpr19-858/novelty-detection.git
cd novelty-detection
This code runs on Python 3.6.
The easiest way to set up the environment is via pip
and the file requirements.txt
:
pip install -r requirements.txt
MNIST and CIFAR-10 will be downloaded for you by torchvision.
You still need to download UCSD Ped and
ShanghaiTech. After download, please unpack them into the data
folder as follows
tar -xzvf <path-to-UCSD_Anomaly_Dataset.tar.gz> -C data
tar -xzvf <path-to-shanghaitech.tar.gz> -C data
Checkpoints for all trained models are available here.
Please untar them into the checkpoints
folder as follows:
tar -xzvf <path-to-tar.gz> -C checkpoints
Once your setup is complete, running tests is as simple as running test.py
.
Usage:
usage: test.py [-h]
positional arguments:
The name of the dataset to perform tests on.Choose among
`mnist`, `cifar10`, `ucsd-ped2`, `shanghaitech`
optional arguments:
-h, --help show this help message and exit
Example:
python test.py ucsd-ped2