Official implementation of "Learning Not to Reconstruct" (BMVC 2021)

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Deep Learning LearningNotToReconstructAnomalies
Overview

Official PyTorch implementation of "Learning Not to Reconstruct Anomalies"

This is the implementation of the paper "Learning Not to Reconstruct Anomalies" (BMVC 2021).

Dependencies

  • Python 3.6
  • PyTorch = 1.7.0
  • Numpy
  • Sklearn

Datasets

  • USCD Ped2 [dataset]
  • CUHK Avenue [dataset]
  • ShanghaiTech [dataset]
  • CIFAR-100 (for patch based pseudo anomalies)
  • ImageNet (for patch based pseudo anomalies)

Download the datasets into dataset folder, like ./dataset/ped2/, ./dataset/avenue/, ./dataset/shanghai/, ./dataset/cifar100/, ./dataset/imagenet/

Training

git clone https://github.com/aseuteurideu/LearningNotToReconstructAnomalies
  • Training baseline
python train.py --dataset_type ped2
  • Training patch based model
python train.py --dataset_type ped2 --pseudo_anomaly_cifar_inpainting_smoothborder 0.2 --max_size 0.5 --max_move 10
  • Training skip frame based model
python train.py --dataset_type ped2 --pseudo_anomaly_jump_inpainting 0.2 --jump 2 3 4 5

Select --dataset_type from ped2, avenue, or shanghai.

For more details, check train.py

Pre-trained models

  • Model in Table 1
Model Dataset AUC Weight
Baseline Ped2 92.49% [ drive ]
Baseline Avenue 81.47% [ drive ]
Baseline ShanghaiTech 71.28% [ drive ]
Patch based Ped2 94.77% [ drive ]
Patch based Avenue 84.91% [ drive ]
Patch based ShanghaiTech 72.46% [ drive ]
Skip frame based Ped2 96.50% [ drive ]
Skip frame based Avenue 84.67% [ drive ]
Skip frame based ShanghaiTech 75.97% [ drive ]
  • Various patch based models on Ped2 (Fig. 5(c))
Intruder Dataset Patching Technique AUC Weight
CIFAR-100 SmoothMixS 94.77% [ drive ]
ImageNet SmoothMixS 93.34% [ drive ]
ShanghaiTech SmoothMixS 94.74% [ drive ]
Ped2 SmoothMixS 94.15% [ drive ]
CIFAR-100 SmoothMixC 94.22% [ drive ]
CIFAR-100 CutMix 93.54% [ drive ]
CIFAR-100 MixUp-patch 94.52% [ drive ]

Evaluation

  • Test the model
python evaluate.py --dataset_type ped2 --model_dir path_to_weight_file.pth
  • Test the model and save result image
python evaluate.py --dataset_type ped2 --model_dir path_to_weight_file.pth --img_dir folder_path_to_save_image_results
  • Test the model and generate demonstration video frames
python evaluate.py --dataset_type ped2 --model_dir path_to_weight_file.pth --vid_dir folder_path_to_save_video_results

Then compile the frames into video. For example, to compile the first video in ubuntu:

ffmpeg -framerate 10 -i frame_00_%04d.png -c:v libx264 -profile:v high -crf 20 -pix_fmt yuv420p video_00.mp4

Bibtex

@inproceedings{astrid2021learning,
  title={Learning Memory-guided Normality for Anomaly Detection},
  author={Astrid, Marcella and Zaheer, Muhammad Zaigham and Lee, Jae-Yeong and Lee, Seung-Ik},
  booktitle={BMVC},
  year={2021}
}

Acknowledgement

The code is built on top of code provided by Park et al. [ github ] and Gong et al. [ github ]

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