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

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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

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/

Update

  • 02/04/21: We uploaded the codes based on reconstruction method, and pretrained wieghts for Ped2 reconstruction, Avenue prediction and Avenue reconstruction.

Training

  • The training and testing codes are based on prediction method
  • Now you can implemnet the codes based on both prediction and reconstruction methods.
  • The codes are basically based on the prediction method, and you can easily implement this as
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'
  • For the reconstruction task, you need to newly set the parameters, e.g,, the target task, the weights of the losses and the number of the time sequence.
python Train.py --method recon --loss_compact 0.01 --loss_separate 0.01 --t_length 1 # for training

Evaluation

  • 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
  • For the reconstruction task, you need to set the parameters as
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
  • 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

Pre-trained model and memory items

Will be released soon.

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}
}