DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection
Haoyang He1#, Jiangning Zhang1,2#, Hongxu Chen1, Xuhai Chen1, Zhishan Li1, Xu Chen2, Yabiao Wang2, Chengjie Wang2, Lei Xie1*
(#Equal contribution, *Corresponding author)
1College of Control Science and Engineering, Zhejiang University, 2Youtu Lab, Tencent
[Paper]
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Our DiAD will also be supported in ADer
- We update Multi-class DiAD results in MVTec-AD/VisA/Real-IAD/Uni-Medical/COCO-AD/MVTec-3D datasets for seven metrics.
Reconstruction-based approaches have achieved remarkable outcomes in anomaly detection. The exceptional image reconstruction capabilities of recently popular diffusion models have sparked research efforts to utilize them for enhanced reconstruction of anomalous images. Nonetheless, these methods might face challenges related to the preservation of image categories and pixel-wise structural integrity in the more practical multi-class setting. To solve the above problems, we propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection, which consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion’s denoising network, and a feature-space pre-trained feature extractor. Firstly, The SG network is proposed for reconstructing anomalous regions while preserving the original image’s semantic information. Secondly, we introduce Spatial-aware Feature Fusion (SFF) block to maximize reconstruction accuracy when dealing with extensively reconstructed areas. Thirdly, the input and reconstructed images are processed by a pre-trained feature extractor to generate anomaly maps based on features extracted at different scales. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach which surpasses the state-of-the-art methods, e.g., achieving 96.8/52.6 and 97.2/99.0 (AUROC/AP) for localization and detection respectively on multi-class MVTec-AD dataset.
First create a new conda environment
conda env create -f environment.yaml
conda activate diad
pip3 install timm==0.8.15dev0 mmselfsup pandas transformers openpyxl imgaug numba numpy tensorboard fvcore accimage Ninja
- Create the MVTec-AD dataset directory. Download the MVTec-AD dataset from MVTec-AD. Unzip the file and move them to
./training/MVTec-AD/. The MVTec-AD dataset directory should be as follows.
|-- training
|-- MVTec-AD
|-- mvtec_anomaly_detection
|-- bottle
|-- ground_truth
|-- broken_large
|-- 000_mask.png
|-- broken_small
|-- 000_mask.png
|-- contamination
|-- 000_mask.png
|-- test
|-- broken_large
|-- 000.png
|-- broken_small
|-- 000.png
|-- contamination
|-- 000.png
|-- good
|-- 000.png
|-- train
|-- good
|-- 000.png
|-- train.json
|-- test.json
- Create the VisA dataset directory. Download the VisA dataset from VisA_20220922.tar. Unzip the file and move them to
./training/VisA/. The VisA dataset directory should be as follows.
|-- training
|-- VisA
|-- visa
|-- candle
|-- Data
|-- Images
|-- Anomaly
|-- 000.JPG
|-- Normal
|-- 0000.JPG
|-- Masks
|--Anomaly
|-- 000.png
|-- visa.csv
- Finetune the Autoencoders first by downloading the pretrained Autoencoders from kl-f8.zip. Move it to
./models/autoencoders.ckpt. And finetune the model with running
python finetune_autoencoder.py
- Once finished the finetuned model is under the folder
./lightning_logs/version_x/checkpoints/epoch=xxx-step=xxx.ckpt. Then move it to the folder with changed name./models/mvtec_ae.ckpt. The same finetune process on VisA dataset. - If you use the given pretrained autoencoder model, you can go step 4 to build the model.
| Autoencoder | Pretrained Model |
|---|---|
| MVTec First Stage Autoencoder | mvtecad_fs |
| VisA First Stage Autoencoder | visa_fs |
- We use the pre-trianed stable diffusion v1.5, the finetuned autoencoders and the Semantic-Guided Network to build the full needed model for training.
The stable diffusion v1.5 could be downloaded from "v1-5-pruned.ckpt". Move it under the folder
./models/v1-5-pruned.ckpt. Then run the code to get the output model./models/diad.ckpt.
python build_model.py
- Training the model by simply run
python train.py
- Batch size, learning rate, data path, gpus, and resume path could be easily edited in
train.py.
The output of the saved checkpoint could be saved under ./val_ckpt/epoch=xxx-step=xxx.ckptFor evaluation and visualization, set the checkpoint path --resume_path and run the following code:
python test.py --resume_path ./val_ckpt/epoch=xxx-step=xxx.ckpt
The images are saved under `./log_image/, where
xxx-input.jpgis the input image.xxx-reconstruction.jpgis the reconstructed image through autoencoder without diffusion model.xxx-features.jpgis the feature map of the anomaly score.xxx-samples.jpgis the reconstructed image through the autoencoder and diffusion model.xxx-heatmap.pngis the heatmap of the anomaly score.
| Method | mAU-ROCI | mAPI | mF1-maxI | mAU-ROCP | mAPP | mF1-maxP | mAU-PROR |
|---|---|---|---|---|---|---|---|
| MVTec-AD | 97.2 | 99.0 | 96.5 | 96.8 | 52.6 | 55.5 | 90.7 |
| VisA | 86.8 | 88.3 | 85.1 | 96.0 | 26.1 | 33.0 | 75.2 |
| Real-IAD | 75.6 | 66.4 | 69.9 | 88.0 | 2.9 | 7.1 | 58.1 |
| Uni-Medical | 85.1 | 84.5 | 81.2 | 95.9 | 38.0 | 35.6 | 85.4 |
| COCO-AD | 59.0 | 53.0 | 63.2 | 68.1 | 20.5 | 14.2 | 30.8 |
| MVTec-3D | 84.6 | 94.8 | 95.6 | 96.4 | 25.3 | 32.3 | 87.8 |
If you find this code useful, don't forget to star the repo and cite the paper:
@inproceedings{he2024diffusion,
title={A Diffusion-Based Framework for Multi-Class Anomaly Detection},
author={He, Haoyang and Zhang, Jiangning and Chen, Hongxu and Chen, Xuhai and Li, Zhishan and Chen, Xu and Wang, Yabiao and Wang, Chengjie and Xie, Lei},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={38},
number={8},
pages={8472--8480},
year={2024}
}
We thank the great works UniAD, LDM and ControlNet for providing assistance for our research.