GitHub - zhangzjn/ADer: ADer is an open source visual anomaly detection toolbox based on PyTorch, which supports multiple popular AD datasets and approaches.

ADer is an open source visual Anomaly Detection toolbox based on PyTorch, which supports multiple popular AD datasets and approaches.

We reproduce popular AD methods under the Multi-class Unsupervised Anomaly Detection (MUAD) by default.

We hope it can bring convenience to your research and application.

🐉 News

🔥 COCO-AD and powerful InvAD is released 🐲 Paper | Project | Code
🔥 Mamba based MambaAD is released 🐲 Paper | Project | Code
🔥 Plain ViT based ViTAD is released 🐲 Paper | Project | Code
🔥 Real-IAD is released: a new large-scale challenging industrial AD dataset 🐲 Paper | Project | Code

💡 Property

🚀 Multi-/Single-class Training and Testing
🚀 DDP Training
🚀 Convenient and flexible way to implement a new approach, refer to here.
🚀 ViTAD
🚀 InvAD: download wide_resnet50_2 in TIMM to model/pretrain
Reproduced popular counterparts:
- 🚀 UniAD, NeurIPS'22: download efficientnet_b4 in TIMM to model/pretrain
- DiAD, AAAI'24
- MambaAD, arXiv'24

🛠️ Getting Started

Installation

Clone this repo:

git clone https://github.com/zhangzjn/ader.git && cd ader

Prepare general experimental environment

pip3 install timm==0.8.15dev0 mmselfsup pandas transformers openpyxl imgaug numba numpy tensorboard fvcore accimage Ninja
pip3 install mmdet==2.25.3
pip3 install --upgrade protobuf==3.20.1 scikit-image faiss-gpu
pip3 install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu118
(or) conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=11.8 -c pytorch -c nvidia

Dataset Preparation

Please refer to Datasets Description for preparing visual AD datasets as needed.

Real-IAD: A new large-scale challenging industrial AD dataset, containing 30 classes with totally 151,050 images; 2,000 ∼ 5,000 resolution; 0.01% ~ 6.75% defect proportions; 1:1 ~ 1:10 defect ratio.
COCO-AD: Large-scale and general-purpose challenging AD-adapted dataset.
MVTec AD: Most popular AD dataset.
VisA: Popular AD dataset.
Uni-Medical: Unified medical AD dataset.
(Opt.) MVTec 3D-AD: Improved 3D version of MVTec AD.
(Opt.) Cifar10 & Cifar100: For one-class-train, one-class-test, and unified settings.

Train (Multi-class Unsupervised AD setting by default, MUAD)

Check data and model settings for the config file configs/METHOD/METHOD_CFG.py
Train with single GPU example: CUDA_VISIBLE_DEVICES=0 python run.py -c configs/METHOD/METHOD_cfg.py -m train
Train with multiple GPUs (DDP) in one node:
- export nproc_per_node=8
- export nnodes=1
- export node_rank=0
- export master_addr=YOUR_MACHINE_ADDRESS
- export master_port=12315
- python -m torch.distributed.launch --nproc_per_node=$nproc_per_node --nnodes=$nnodes --node_rank=$node_rank --master_addr=$master_addr --master_port=$master_port --use_env run.py -c configs/METHOD/METHOD_CFG.py -m train.
Modify trainer.resume_dir to resume training.
Single-class Unsupervised AD (SUAD, not recommend currently)
1. one GPU-0 for training one grid class in mvtec dataset: CUDA_VISIBLE_DEVICES=0 python3 run.py -c configs/vitad/single_cls/vitad_mvtec_bs16.py -m train data.cls_names=grid trainer.checkpoint=runs/vitad/single_class/vitad_mvtec_bs16/grid
2. one GPU-0 for training all classes serially in mvtec dataset: python3 runs_single_class.py -d mvtec -c configs/vitad/single_cls/vitad_mvtec_bs16.py -n 1 -m -1 -g 0
3. $GPU_NUM GPUs for training all classes parallelly in mvtec dataset:: python3 runs_single_class.py -d mvtec -c configs/vitad/single_cls/vitad_mvtec_bs16.py -n $GPU_NUM -m 1
4. results will be saved in default dir: runs/vitad/single_cls/vitad_mvtec_bs16

Test

Modify trainer.resume_dir or model.kwargs['checkpoint_path']
Test with single GPU example: CUDA_VISIBLE_DEVICES=0 python run.py -c configs/METHOD/METHOD_cfg.py -m test
Test with multiple GPUs (DDP) in one node: python -m torch.distributed.launch --nproc_per_node=$nproc_per_node --nnodes=$nnodes --node_rank=$node_rank --master_addr=$master_addr --master_port=$master_port --use_env run.py -c configs/METHOD/METHOD_CFG.py -m test.

How to Build a Custom Approach

Add a model config cfg_model_MODEL_NAME to configs/__base__
Add configs to configs/MODEL_NAME/CFG.py for training and testing.
Add a model implementation file model/MODEL_NAME.py
Add a trainer implementation file trainer/MODEL_NAME_trainer.py
(Optional) Add specific files to data, loss, optim, etc.

📜 MUAD Results on Popular AD Datasets

Red metrics are recommended for comprehensive evaluations.
Subscripts I, R, and P represent image-level, region-level, and pixel-level, respectively.

MVTec AD

Method	mAU-ROC_I	mAP_I	mF1-max_I	mAU-PRO_R	mAU-ROC_P	mAP_P	mF1-max_P	mF1_P/.2/.8	mAcc_P/.2/.8	mIoU_P/.2/.8	mIoU-max_P	mAD_I	mAD_P	mAD_.2/.8	mAD	Download
UniAD	97.5	99.1	97.3	90.7	97.0	45.1	50.4	22.4	37.5	13.9	34.2	98.0	64.1	24.6	82.4	log & weight
DiAD	97.2	99.0	96.5	90.7	96.8	52.6	55.5	19.5	40.7	12.0	21.3	97.6	68.3	24.1	84.0	log & weight
ViTAD	98.3	99.4	97.3	91.4	97.7	55.3	58.7	30.9	40.8	20.4	42.6	98.3	70.6	30.7	85.4	log & weight
InvAD	98.9	99.6	98.1	94.1	98.2	57.6	60.1	34.6	46.9	23.0	43.7	98.9	72.0	34.8	86.7	log & weight
InvAD-lite	98.2	99.2	97.2	97.3	55.0	58.1	92.7	32.6	47.1	21.3	41.7	98.2	68.6	33.7	85.4	log & weight

VisA

Method	mAU-ROC_I	mAP_I	mF1-max_I	mAU-PRO_R	mAU-ROC_P	mAP_P	mF1-max_P	mF1_P/.2/.8	mAcc_P/.2/.8	mIoU_P/.2/.8	mIoU-max_P	mAD_I	mAD_P	mAD_.2/.8	mAD	Download
UniAD	88.8	90.8	85.8	85.5	98.3	33.7	39.0	17.9	47.1	10.9	25.7	88.4	57.0	25.3	74.5	log & weight
DiAD	86.8	88.3	85.1	75.2	96.0	26.1	33.0	13.2	46.2	8.0	16.2	86.7	51.7	22.5	70.1	log & weight
ViTAD	90.5	91.7	86.3	85.1	98.2	36.6	41.1	21.6	38.2	13.5	27.6	89.5	58.7	24.4	75.6	log & weight
InvAD	95.5	95.8	92.1	92.5	98.9	43.1	47.0	28.0	45.6	17.9	32.7	94.5	63.0	30.5	80.7	log & weight
InvAD-lite	94.9	95.2	90.8	98.6	40.3	44.3	92.5	25.8	44.2	16.1	30.0	93.6	59.0	28.7	79.5	log & weight

COCO-AD

Method	mAU-ROC_I	mAP_I	mF1-max_I	mAU-PRO_R	mAU-ROC_P	mAP_P	mF1-max_P	mF1_P/.2/.8	mAcc_P/.2/.8	mIoU_P/.2/.8	mIoU-max_P	mAD_I	mAD_P	mAD_.2/.8	mAD	Download
UniAD	56.2	49.0	61.8	31.7	65.4	12.9	19.4	6.6	26.3	3.7	11.1	55.7	32.6	12.2	42.3	log & weight
DiAD	59.0	53.0	63.2	30.8	68.1	20.5	14.2	9.6	31.1	6.1	11.6	58.4	34.2	15.6	44.1	log & weight
ViTAD	69.3	60.4	64.9	41.0	78.3	27.9	31.9	12.4	37.4	7.2	19.8	64.9	46.0	19.0	53.4	log & weight
InvAD	65.9	57.8	64.1	44.9	73.2	19.7	25.4	12.4	37.5	7.1	15.2	62.6	39.4	19.0	50.1	log & weight
InvAD-lite	64.7	56.7	63.5	70.5	18.3	23.4	38.2	11.2	34.2	6.4	13.8	61.6	26.6	17.3	47.9	log & weight

MVTec 3D-AD (RGB)

Method	mAU-ROC_I	mAP_I	mF1-max_I	mAU-PRO_R	mAU-ROC_P	mAP_P	mF1-max_P	mF1_P/.2/.8	mAcc_P/.2/.8	mIoU_P/.2/.8	mIoU-max_P	mAD_I	mAD_P	mAD_.2/.8	mAD	Download
UniAD	78.9	93.4	91.4	88.1	96.5	21.2	28.0	12.2	43.6	7.0	16.8	87.9	48.6	20.9	71.1	log & weight
DiAD	84.6	94.8	95.6	87.8	96.4	25.3	32.3	5.0	71.4	2.6	5.4	91.7	51.3	26.3	73.8	log & weight
ViTAD	79.0	93.1	91.8	91.6	98.2	27.3	33.3	17.2	45.3	10.0	20.5	88.0	52.9	24.1	73.5	log & weight
InvAD	86.1	95.8	93.2	94.7	98.8	37.8	42.5	22.0	50.5	13.2	27.5	91.7	59.7	28.6	78.4	log & weight
InvAD-lite	85.3	95.2	93.0	98.6	37.2	41.4	94.1	21.6	55.3	12.9	26.5	91.2	57.6	30.0	77.8	log & weight

Uni-Medical

Method	mAU-ROC_I	mAP_I	mF1-max_I	mAU-PRO_R	mAU-ROC_P	mAP_P	mF1-max_P	mF1_P/.2/.8	mAcc_P/.2/.8	mIoU_P/.2/.8	mIoU-max_P	mAD_I	mAD_P	mAD_.2/.8	mAD	Download
UniAD	78.5	75.2	76.6	96.4	37.6	40.2	85.0	13.3	37.8	8.0	26.8	76.8	54.3	19.7	69.9	log & weight
DiAD	85.1	84.5	81.2	85.4	95.9	38.0	35.6	19.0	57.6	11.4	25.0	83.6	56.5	29.3	72.2	log & weight
ViTAD	82.2	81.0	80.1	97.2	49.9	49.6	86.1	18.6	36.5	11.7	35.1	81.1	61.9	22.3	75.2	log & weight
InvAD	82.2	79.6	80.6	97.4	47.5	47.1	89.6	21.8	45.2	13.8	33.3	80.8	61.4	26.9	74.9	log & weight
InvAD-lite	79.5	78.3	79.1	96.4	40.1	40.4	85.5	18.3	40.5	11.2	27.6	79.0	55.3	23.3	71.3	log & weight

Real-IAD

Method	mAU-ROC_I	mAP_I	mF1-max_I	mAU-PRO_R	mAU-ROC_P	mAP_P	mF1-max_P	mF1_P/.2/.8	mAcc_P/.2/.8	mIoU_P/.2/.8	mIoU-max_P	mAD_I	mAD_P	mAD_.2/.8	mAD	Download
UniAD	82.9	80.8	74.4	97.4	22.9	30.3	86.4	10.5	35.0	6.0	18.3	79.4	46.5	17.2	67.9	log & weight
DiAD	75.6	66.4	69.9	58.1	88.0	2.9	7.1	2.9	41.9	1.5	3.7	70.6	32.7	15.4	52.6	log & weight
ViTAD	82.7	80.1	73.7	97.3	24.2	32.3	83.9	13.4	27.3	7.7	19.6	78.8	46.8	16.1	67.8	log & weight
InvAD	89.0	86.4	79.6	98.4	30.7	37.6	91.9	17.7	36.1	10.4	23.5	85.0	53.4	21.4	73.4	log & weight
InvAD-lite	87.2	85.1	77.8	98.1	31.6	37.9	91.6	17.6	36.8	10.3	23.7	83.3	53.7	21.6	72.7	log & weight

Citation

If you use this toolbox or benchmark in your research, please cite our related works.

@inproceedings{realiad,
  title={Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection},
  author={Wang, Chengjie and Zhu, Wenbing and Gao, Bin-Bin and Gan, Zhenye and Zhang, Jianning and Gu, Zhihao and Qian, Shuguang and Chen, Mingang and Ma, Lizhuang},
  booktitle={CVPR},
  year={2024}
}

@article{vitad,
  title={Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection},
  author={Zhang, Jiangning and Chen, Xuhai and Wang, Yabiao and Wang, Chengjie and Liu, Yong and Li, Xiangtai and Yang, Ming-Hsuan and Tao, Dacheng},
  journal={arXiv preprint arXiv:2312.07495},
  year={2023}
}

@article{invad,
  title={Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark},
  author={Jiangning Zhang and Chengjie Wang and Xiangtai Li and Guanzhong Tian and Zhucun Xue and Yong Liu and Guansong Pang and Dacheng Tao},
  journal={arXiv preprint arXiv:2404.10760},
  year={2024}
}

@article{mambaad,
  title={MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection},
  author={He, Haoyang and Bai, Yuhu and Zhang, Jiangning and He, Qingdong and Chen, Hongxu and Gan, Zhenye and Wang, Chengjie and Li, Xiangtai and Tian, Guanzhong and Xie, Lei},
  year={2024}
}

Name		Name	Last commit message	Last commit date
Latest commit History 10 Commits
assets		assets
configs		configs
data		data
loss		loss
model		model
optim		optim
trainer		trainer
util		util
.DS_Store		.DS_Store
README.md		README.md
__init__.py		__init__.py
run.py		run.py
run.sh		run.sh
runs_single_class.py		runs_single_class.py

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