Adversarially Learned One-Class Classifier for Novelty Detection (ALOCC-CVPR2018)

• Install pillow,scipy,skimage,imageio,numpy,matplotlib

pip install numpy scipy scikit-image imageio matplotlib pillow

ALOCC train

• Download a UCSD dataset:

mkdir dataset
cd dataset
wget http://www.svcl.ucsd.edu/projects/anomaly/UCSD_Anomaly_Dataset.tar.gz
tar -xzf UCSD_Anomaly_Dataset.tar.gz

• Config path of dataset and dataset name :

# for train on UCSD and patch_size 30*30
python train.py

<hr>


### ALOCC test
- You can run the following commands:

For test on UCSD and patch_size 30*30 and some specific dir like ['Test004'], etc. We prefer to open test.py file and edit every thing that you want

python test.py

---

For changing the patch size, change the input_height from 30

Apply a pre-trained model (ALOCC)

• The pretrained model is saved in checkpoints directory

Saved model can be found in https://drive.google.com/open?id=1oprESeLKbbt2Fwse0K9vx1FxiftjmllA

At the time of testing, change the checkpoint manually in f_check_checkpoint function in models.py
<hr>

Anomalies of the directories are dumped in anomalies folder.