/Diffusion_Multi-View_AD

Learning Diffusion Models for Multi-View Anomaly Detection [ECCV2024]

Primary LanguagePython

Learning Diffusion Models for Multi-View Anomaly Detection

Installation

Requirement

Linux (Ubuntu 16.04)
Python 3.9+
PyTorch 1.7 or higher
CUDA 10.2 or higher

create environment

git clone https://github.com/jayliu0313/Multi_Lightings.git
cd Multi_Lightings
conda create --name myenv python=3.6
conda activate myenv
pip install -r requirements.txt

Eyecandies Dataset

Here to download dataset

MvTec3D-AD Dataset

Here to download dataset

Implementation

Finetune the UNet and CountrolNet

python train_unifiedunet.py --data_path DATASET_PATH --ckpt_path SAVE_PATH

python train_controlnet.py --data_path DATASET_PATH --load_unet_ckpt UNET_CKPT_PATH --ckpt_path SAVE_PATH

Buid Memory and Inference

The result will be stored in the output directory. You can use "--vis" to visualize our result of the heat map.

python test.py --datasets_path DATASET_PATH --grid_path data/ --ckpt_path CKPT_PATH

Reference

Our memory architecture is refer to https://github.com/eliahuhorwitz/3D-ADS
3D expert model is modified from https://github.com/mabaorui/PredictableContextPrior