This is an unofficial implementation of paper "Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder (MemAE) for Unsupervised Anomaly Detection".
Majority of the code are based on the original repo https://github.com/donggong1/memae-anomaly-detection
| Dataset | Paper | This implementation |
|---|---|---|
| UCSDped2 | 94.1 | 94.0 |
| Avenue | 83.3 | 81.0 |
-
PyTorch == 1.4.0
-
Python==3.7.6
-
./requirement.sh
prepare_data.sh dataset datapath
dataset: Avenue, UCSDped2
datapath: the path that you want to save the data, i.e., /project/anomaly_data/
./run.sh dataset datapath expdir
dataset: Avenue, UCSDped2
datapath: the path that you want to save the data, i.e., /project/anomaly_data/
expdir: the path that you want to save the checkpoint
./eval.sh dataset, datapath, version, ckpt, expdir
dataset: Avenue, UCSDped2
datapath: the path that you saved the data
version: experiment version
ckpt: the checkpoint step
expdir: the path that you saved the model checkpoint
If your Avenue dataset is saved under /project/anomaly_data/Avenue/frames/testing/...., run
./eval.sh Avenue /project/anomaly_data/ 0 40 ckpt/ to get the reported performance
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent