[ECCV 2024] TransFusion

This repository holds the official Pytorch implementation for the paper TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection accepted at ECCV 2024. Journal paper about the extension to 3D and improvements to the base model coming soon.

Abstract

Surface anomaly detection is a vital component in manufacturing inspection. Current discriminative methods follow a two-stage architecture composed of a reconstructive network followed by a discriminative network that relies on the reconstruction output. Currently used reconstructive networks often produce poor reconstructions that either still contain anomalies or lack details in anomaly-free regions. Discriminative methods are robust to some reconstructive network failures, suggesting that the discriminative network learns a strong normal appearance signal that the reconstructive networks miss. We reformulate the two-stage architecture into a single-stage iterative process that allows the exchange of information between the reconstruction and localization. We propose a novel transparency-based diffusion process where the transparency of anomalous regions is progressively increased, restoring their normal appearance accurately while maintaining the appearance of anomaly-free regions using localization cues of previous steps. We implement the proposed process as TRANSparency DifFUSION (TransFusion), a novel discriminative anomaly detection method that achieves state-of-the-art performance on both the VisA and the MVTec AD datasets, with an image-level AUROC of 98.5% and 99.2%, respectively.

TransFusion Environment Setup Guide

To install and set up the TransFusion environment, follow these steps:

   conda create --name TransFusion --file requirements.txt
   conda activate TransFusion

Datasets

1. Download Anomaly Detection Datasets from the following links: MVtec3D, VisA and MVtec AD

2. (For training only) Downloade the DTD Dataset from the following link: DTD

3. (For training only) Download foreground masks using download_fg_masks.sh inside the scripts folder

Training

For training on the MVTec3D dataset use the following command (and exchange the paths for the correct ones):

   python Experiment.py -c train -r RUN_NAME -d ./datasets/mvtec3d/ -ds mvtec3d --mode rgbd --dtd-data-path ./datasets/dtd/images/ 

For training on VisA or MVTec AD dataset use the following command (exchange the paths for the correct ones and DATASET for either visa or mvtec):

   python Experiment.py -c train -r RUN_NAME -d ./datasets/DATASET/ -ds DATASET --mode rgb --dtd-data-path ./datasets/dtd/images/

For changes to other parameters check the Argparser.py inside utils.

Testing

To evaluate the model on MVTec 3D use the following command:

   python Experiment.py -c test -r RUN_NAME -d ./datasets/mvtec3d/ -ds mvtec3d --mode rgbd

For VisA or MVTec use use the following command (exchnage DATASET for either visa or mvtec):

   python Experiment.py -c test -r RUN_NAME -d ./datasets/DATASET/ -ds DATASET --mode rgb

If you want result visualization add the --visualize at the end.

Available Model Weights

Here is a list of currently avaible model weights (all attained at last epoch) and their download links. Preferably download the models using the scripts inside the scripts which also setup the correct folder structure for testing. The run names for the weights downloaded with the scripts are transfusion_mvtec3d, transfusion_visa and transfusion_mvtec.

Dataset	Model Weights	Image-level AUROC	AUPRO
MVTec 3D	Download	98.2	98.3
VisA	Download	98.7	94.7
MVTec AD	Download	99.4	95.3

Citation

If you use TransFusion in your research, please cite the following paper:

@InProceedings{Fucka_2024_ECCV,
  title={Trans{F}usion -- {A} {T}ransparency-{B}ased {D}iffusion {M}odel for {A}nomaly {D}etection},
  author={Fu{\v{c}}ka, Matic and Zavrtanik, Vitjan and Sko{\v{c}}aj, Danijel},
  booktitle={European conference on computer vision},
  year={2024},
  month={October}
}