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[ECCV 2024] Few-Shot Anomaly-Driven Generation for Anomaly Detection

Introduction

This repository is an official PyTorch implementation of Few-Shot Anomaly-Driven Generation for Anomaly Classification and Segmentation (ECCV 2024).

This repository mainly consists of two parts.

The first part involves generating a large scale of realistic and diverse anomaly images based on the LDM model, using few-shot real abnormal images.

The second part involves using the generated anomaly images to replace the synthetic anomalies in existing methods (DRAEM and DesTSeg) to train an anomaly detection model, thereby validating the effectiveness of the generated anomalies for downstream tasks.

Preparation

Download MVTec anomaly detection dataset

mkdir ./datasets/mvtec
cd ./datasets/mvtec
wget https://www.mydrive.ch/shares/38536/3830184030e49fe74747669442f0f282/download/420938113-1629952094/mvtec_anomaly_detection.tar.xz
tar -xf mvtec_anomaly_detection.tar.xz
rm mvtec_anomaly_detection.tar.xz

Download pre-trained diffusion model

cd DIFFUSION
mkdir -p models/ldm/text2img-large/
wget -O models/ldm/text2img-large/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/txt2img-f8-large/model.ckpt

Download Generated Anomaly Images optional

Stage1: Learn Embedding with Few-Shot Anomaly Images

cd DIFFUSION
categories=("broken_large" "broken_small" "contamination")
words=("broken" "broken" "broken")
for ((i=0;i<3;i++))
do
    name="v2_bottle_${categories[$i]}" 
    path="mvtec_train_data/bottle/${categories[$i]}"
    word=${words[$i]}
    python main.py \
    --base configs/latent-diffusion/txt2img-1p4B-finetune.yaml \
    -t --actual_resume models/ldm/text2img-large/model.ckpt \
    -n $name \
    --gpus 0, \
    --data_root $path \
    --init_word "defect"
done

Given support images for all categories, you can train the embedding for each defect.

cd DIFFUSION
sh train.sh

Stage2: Few-Shot Anomaly-Driven Generation

Given a normal image and the expected defect bounding box, you can generate an anomaly image using the learned embedding and the pre-trained diffusion model.

cd DIFFUSION

python scripts/txt2img.py \
            --ddim_eta 0.0 \
            --n_samples 1 \
            --n_iter 2 \
            --scale 10.0 \
            --ddim_steps 50 \
            --embedding_path "logs/bottle_broken_large/embeddings.pt" \
            --ckpt_path "models/ldm/text2img-large/model.ckpt" \
            --prompt "*" \
            --mask_prompt "images/demo_images/mask.png" \
            --image_prompt "images/demo_images/bottle.png" \
            --outdir "outputs"

Stage3: Weakly-Supervised Anomaly Detection

You can download the generated anomaly images for your model training.

Train DREAM model with generated images

cd DREAM
sh ./train.sh
sh ./test.sh

Train DREAM model with generated images

cd DeSTSeg
sh train.sh
sh test.sh

Citing

If you find this code useful in your research, please consider citing us:

@inproceedings{2024anogen,
  title={Few-Shot Anomaly-Driven Generation for Anomaly Classification and Segmentation},
  author={Gui, Guan and Gao, Bin-Bin and Liu, Jun and Wang, Chengjie and Wu, Yunsheng},
  booktitle={European Conference on Computer Vision (ECCV 2024)},
  pages={--},
  year={2024}
}