Implementation of CutPaste
This is a unofficial work in progress PyTorch reimplementation of CutPaste: Self-Supervised Learning for Anomaly Detection and Localization and in no way affiliated with the original authors. Use at own risk. Pull requestes and feedback is appreciated.
Setup
Download the MVTec Anomaly detection Dataset from here and extract it into a new folder named Data.
Install the following requirements:
- Pytorch and torchvision
- sklearn
- pandas
- seaborn
- tqdm
- tensorboard
For example with Anaconda:
conda create -n cutpaste pytorch torchvision torchaudio cudatoolkit=10.2 seaborn pandas tqdm tensorboard scikit-learn -c pytorch
conda activate cutpaste
Run Training
python run_training.py --model_dir models --head_layer 2
The Script will train a model for each defect type and save it in the model_dir Folder.
One can track the training progress of the models with tensorboard:
tensorboard --logdir logdirs
Run Evaluation
python eval.py --model_dir models --head_layer 2
This will create a new directory Eval with plots for each defect type/model.
Some implementation details
Only the normal CutPaste augmentation and 2-Class classification variant is implemented.
The pasted image patch always origins from the same image it is pasted to. I'm not sure if this is a Problem and if this is also the case in the original paper/code.
TODOs
- implement Cut-Paste Scar
- implement gradCam
- implement localization variant
- add option to finetune on EfficientNet(B4)
- clean up parameters and move them into the arguments of the scripts
- compare results of this reimplementation with the results of the paper