SINBAD - Set Features for Fine-grained Anomaly Detection

Official PyTorch Implementation for the "Set Features for Fine-grained Anomaly Detection" paper.

Getting Started

Setup

Clone this repo:

git clone https://github.com/NivC/SINBAD.git
cd SINBAD

Create a new environment and install the libraries:

virtualenv -p python3 venv
source venv/bin/activate.csh
pip install -r requirements.txt

Data preparation

Download the data:

mkdir ../dataset_loco
cd ../dataset_loco
wget https://www.mydrive.ch/shares/48237/1b9106ccdfbb09a0c414bd49fe44a14a/download/430647091-1646842701/mvtec_loco_anomaly_detection.tar.xz
tar -xf mvtec_loco_anomaly_detection.tar.xz

Prepare the data:

cd ../SINBAD/
python data_to_matrices.py 
This creates numpy matrices of the data under ../dataset_loco

Single pyramid level run

Create the directory where the anomaly scores and labels will be saved
```
mkdir ../sinbad_runs
```

Run the model for a single pyramid level:

python sinbad_single_layer.py --mvtype breakfast_box_loco  --pyramid_level 7

pyramid_level correspond to the level of the pyramid to be used as follows:
7 - 4th ResNet level (7x7 set elements)
14 - 3rd ResNet level (14x14 set elements)
224 - Raw image pixels (224x224 set elements)

Combine different pyramid levels

This example uses slurm to run the different pyramid levels in parallel.

Create and run scripts to run the different pyramid levels; first the ResNet blocks, and then the raw pixels ("224"):

mkdir ../sinbad_runs/sbatches
python create_batch_array_sinbad.py
cd ../sinbad_runs/sbatches
sh batch_master.sh

mkdir ../sinbad_runs/sbatches_224
python create_batch_array_sinbad_224.py
cd ../sinbad_runs/sbatches_224
sh batch_master.sh

Combine the results from the different pyramid levels in an ensemble:

cd SINBAD
python calc_anom_ensm.py 
--mvtype loco 
--version /path/to/sinbad_runs/results/ver1_pyramid_lvl_#  
--version_224  /path/to/sinbad_runs/results/ver1_pyramid_lvl_#

where mvtype is the class name (or loco/struct/all for a group of classes) and version and version_224 is the path to the results of the different pyramid levels created by the previous script.

Time-series datasets

The implementation for the time-series experiments can be found here: https://github.com/yedidh/radonomaly

Citation

If you find this useful for your research, please use the following citation:

@misc{https://doi.org/10.48550/arxiv.2302.12245,
  doi = {10.48550/ARXIV.2302.12245},  
  url = {https://arxiv.org/abs/2302.12245},
  author = {Cohen, Niv and Tzachor, Issar and Hoshen, Yedid},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Set Features for Fine-grained Anomaly Detection},
  publisher = {arXiv},
  year = {2023},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

NivC / SINBAD

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