For more information, checkout the project site [website] and the paper [PDF].
- Python 3.6
- PyTorch 1.1.0
- Numpy
- Sklearn
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/
- 02/04/21: We uploaded the codes based on reconstruction method, and pretrained wieghts for Ped2 reconstruction, Avenue prediction and Avenue reconstruction.
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- 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-
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}
}
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