Clone Code: git clone https://github.com/sidhartha-roy/anomaly-detection.git

Pre-trained VGG16 convolution layers + new classification layers added. Portion of the convolution layers are frozen.

Color jitter, Random Vertical Flip/ Horizontal Flip/ Rotation. You can try testing the model by training it after changing these parameters.

For data preprocessing please follow instructions in the preprocessing folder.

1. setup a virtual environment named anomaly conda create -n anomaly python=3.6
2. Activate the environment: conda activate anomaly
3. Install PyTorch: conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
4. Install python requirements pip install -r requirements.txt
5. Make sure the directory ./preprocessing/data exists and contains the /normal and /anomaly folders with images.
6. Open the file config.py and setup the parameters for training and testing.
7. Split the data set into train/validation/test folders: python3 split.py
8. Train the model: python3 run_training.py This command downloads vgg16 model, modifies it, creates the dataloader, trains the model, and stores the trained model and history of training in folder /pretrained. Loss history curve is displayed (remember to close window to continue).
9. Test the accuracy of the model: python3 test.py

More model performance features such as ROC and AUC curves can be plotted easily. To play with the code please use the notebook Anomaly_Detection.ipynb