This is the implementation of the paper "Learning Memory-guided Normality for Anomaly Detection (CVPR 2020)".
For more information, checkout the project site [website] and the paper [PDF].
These datasets are from an official github of "Future Frame Prediction for Anomaly Detection - A New Baseline (CVPR 2018)".
Download the datasets into dataset
folder, like ./dataset/ped2/
git clone https://github.com/cvlab-yonsei/projects
cd projects/MNAD/code
python Train.py # for training
python Train.py --gpus 1 --dataset_path 'your_dataset_directory' --dataset_type avenue --exp_dir 'your_log_directory'
python Train.py --method recon --loss_compact 0.01 --loss_separate 0.01 --t_length 1 # for training
python Evaluate.py --dataset_type ped2 --model_dir your_model.pth --m_items_dir your_m_items.pt
python Evaluate.py --method recon --t_length 1 --alpha 0.7 --th 0.015 --dataset_type ped2 --model_dir your_model.pth --m_items_dir your_m_items.pt
python Evaluate.py --dataset_type ped2 --model_dir pretrained_model.pth --m_items_dir m_items.pt
Download our pre-trained model and memory items
[Ped2 Prediction]
[Ped2 Reconstruction]
[Avenue Prediction]
[Avenue Reconstruction]
Note that, you need to set lambda and threshold to 0.7 and 0.015, respectively, for the reconstruction task. See more details in the paper.
@inproceedings{park2020learning,
title={Learning Memory-guided Normality for Anomaly Detection},
author={Park, Hyunjong and Noh, Jongyoun and Ham, Bumsub},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={14372--14381},
year={2020}
}