PyTorch implementation of "Learning Memory-guided Normality for Anomaly Detection"

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].

Dependencies

• Python 3.6
• PyTorch 1.1.0
• Numpy
• Sklearn

Datasets

• USCD Ped2 [dataset]
• CUHK Avenue [dataset]
• ShanghaiTech [dataset]

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/

Training

• The training and testing codes are based on prediction method

git clone https://github.com/cvlab-yonsei/projects
cd projects/MNAD/code
python Train.py # for training

• You can freely define parameters with your own settings like

python Train.py --gpus 1 --dataset_path 'your_dataset_directory' --dataset_type avenue --exp_dir 'your_log_directory'

Pre-trained model and memory items

• Download our pre-trained model and memory items
  Link: [model and items]
• Note that, these are from training with the Ped2 dataset

Evaluation

• Test the model with our pre-trained model and memory items

python Evaluate.py --dataset_type ped2 --model_dir pretrained_model.pth --m_items_dir m_items.pt

• Test your own model
• Check your dataset_type (ped2, avenue or shanghai)

python Evaluate.py --dataset_type ped2 --model_dir your_model.pth --m_items_dir your_m_items.pt

Bibtex

@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}
}