https://github.com/aimagelab/novelty-detection

Latent space autoregression for novelty detection.
https://github.com/aimagelab/novelty-detection

anomaly-detection computer-vision cvpr2019 deep-learning novelty-detection unsupervised-learning

Last synced: 6 days ago
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Latent space autoregression for novelty detection.

• Host: GitHub
• URL: https://github.com/aimagelab/novelty-detection
• Owner: aimagelab
• License: mit
• Created: 2018-11-15T19:28:38.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2022-12-12T11:13:01.000Z (almost 2 years ago)
• Last Synced: 2024-04-19T22:46:51.289Z (7 months ago)
• Topics: anomaly-detection, computer-vision, cvpr2019, deep-learning, novelty-detection, unsupervised-learning
• Language: Python
• Homepage:
• Size: 479 KB
• Stars: 196
• Watchers: 12
• Forks: 59
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 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](images/model.png)

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](http://www.svcl.ucsd.edu/projects/anomaly/UCSD_Anomaly_Dataset.tar.gz) and 

[ShanghaiTech](https://onedrive.live.com/?authkey=%21AMqh2fTSemfrokE&cid=3705E349C336415F&id=3705E349C336415F%2172436&parId=3705E349C336415F%215109&o=OneUp). After download, please unpack them into the `data` folder as follows

```

tar -xzvf  -C data

tar -xzvf  -C data

```

### 3 - Model checkpoints

Checkpoints for all trained models are available [here](https://ailb-web.ing.unimore.it/publicfiles/drive/lsa-novelty-detection/checkpoints.tar.gz).

Please untar them into the `checkpoints` folder as follows:

```

tar -xzvf  -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

```