Awesome

Latent Space Autoregression for Novelty Detection

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}
}

sample results

Specifically, performs:

one class classification on MNIST.
one class classification on CIFAR-10.
video anomaly detection on UCSD Ped2.
video anomaly detection on ShanghaiTech.

0 - Clone this repo

First things first, clone this repository locally via git.

git clone https://github.com/cvpr19-858/novelty-detection.git
cd novelty-detection

1 - Environment

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

2 - Datasets

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

3 - Model checkpoints

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

4 - Run!

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