IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing

We are dedicated to provide researchers a uniform verification environment of image anomaly detection with standard settings and methods. At the same time, everyone is warmly invited to add their algorithms and new features into IM-IAD. Finally, we appreciate all the contributors who maintain this community.

The project is being continuously updated. If any issues are found, please contact us promptly.

[Main Page] [Survey] [Benchmark] [Result]

Envs

pytorch 1.10.1 or 1.8.1
conda activate open-iad
pip3 install -r requirements.txt

# example
pip install scikit-image
pip instll scikit-learn
pip install opencv-python

Project Instruction

IM-IAD
├── arch # model base class
├── augmentation # data augmentation
├── checkpoints # pretrained or requirements
├── configuration
│   ├── 1_model_base # highest priority
│   ├── 2_train_base # middle priority
│   ├── 3_dataset_base # lowest priority
│   ├── config.py # for main.py
│   ├── device.py # for device
│   └── registeration.py # register new model, dataset, server
├── data_io # loading data interface
├── dataset # dataset interface
├── loss_function
├── metric
├── models # basic layers for model class in arch_base
├── optimizer
├── paradigms # learning paradigms
│   ├── centralized
│   │   ├── c2d.py # 2D
│   │   ├── c3d.py # 3D
│   └── federated
│       └── f2d.py # 2D
├── tools
├── work_dir # save results
├── main.py # run start, with configuration/config.py
└── requirements.txt

Dataset (--dataset / -d)

2D: mvtec2d, mpdd, mvtecloco, mtd, btad, mvtec2df3d, coad

3D: mvtec3d

The dataset's structure can be organized as follows (i.e., mvtec2d).

.
├── bottle
│   ├── ground_truth
│   │   ├── broken_large
│   │   │   ├── 000_mask.png
│   │   │   ├── 001_mask.png
│   │   │   ├── ...
│   ├── test
│   │   ├── broken_large
│   │   │   ├── 000.png
│   │   │   ├── 001.png
│   │   │   ├── ...
│   │   └── good
│   │       ├── 000.png
│   │       ├── 001.png
│   │       ├── ...
│   └── train
│       └── good
│           ├── 000.png
│           ├── 001.png
│           ├── ...
├── cable
├── screw
└── ...

Learning Paradigm

	Prototypes	Marker	Train	Test
$\bigstar$	centralized 2d	-p c2d
	vanilla	-v	all data (id=0)	all data (id=0)
	semi	-s	all data (id=0) + anomaly data (id=0)	all data (id=0) - anomaly data (id=0)
	fewshot	-f	fewshot (id=0)	all data (id=0)
	continual	-c	all data (id=0 and 1)	all data (id=0 or 1)
	noisy	-z	all data (id=0) + noisy data (id=0)	all data (id=0) - noisy data (id=0)
	transfer	-t	step 1: all data (id=0)	all data (id=0)
			step 2: fewshot data (id=1)	all data (id=1)
$\bigstar$	centralized 3d	-p c3d	To be updated!
$\bigstar$	federated 2d	-p f2d	To be updated!

2D Model

No.	Method / -m	Net / -n	Paper Title
1	cfa	net_cfa	CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization
2	csflow	net_csflow	Fully convolutional cross-scale-flows for image-based defect detection
3	cutpaste	vit_b_16	Cutpaste: self-supervised learning for anomaly detection and localization
4	devnet	net_devnet	Explainable deep few-shot anomaly detection with deviation networks
5	dne	vit_b_16	Towards continual adaptation in industrial anomaly detection
6	dra	net_dra	Catching both gray and black swans: open-set supervised anomaly detection
7	fastflow	net_fastflow	Fastflow: unsupervised anomaly detection and localization via 2d normalizing flows
8	favae	net_favae	Anomaly localization by modeling perceptual features
9	igd	net_igd	Deep one-class classification via interpolated gaussian descriptor
10	padim	resnet18, wide_resnet50	Padim: a patch distribution modeling framework for anomaly detection and localization
11	patchcore	resnet18, wide_resnet50	Towards total recall in industrial anomaly detection
12	reverse	net_reverse	Anomaly detection via reverse distillation from one-class embedding
13	simplenet	wide_resnet50	SimpleNet: a simple network for image anomaly detection and localization
14	softpatch	resnet18, wide_resnet50	SoftPatch: unsupervised anomaly detection with noisy data
15	spade	resnet18, wide_resnet50	Sub-image anomaly detection with deep pyramid correspondences
16	stpm	resnet18, wide_resnet50	Student-teacher feature pyramid matching for anomaly detection

Run Example

Vanilla / -v

python3 main.py -v -m cfa -n net_cfa -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -v -m csflow -n net_csflow -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -v -m cutpaste -n vit_b_16  -d mvtec2d -tid 0 -vid 0  -g 1
python3 main.py -v -m fastflow -n net_fastflow -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -v -m favae -n net_favae -d mvtec2d -tid 0 -vid 0  -g 1
# python3 main.py -v -m graphcore -n vig_ti_224_gelu -d mvtec2d -tid 0 -vid 0 -sp 0.001 -g 1
python3 main.py -v -m igd -n net_igd -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -v -m padim -n resnet18 -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -v -m cutpaste -n vit_b_16 -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -v -m patchcore -n wide_resnet50 -d mvtec2d -tid 0 -vid 0 -sp 0.001 -g 1
python3 main.py -v -m reverse -n net_reverse -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -v -m simplenet -n wide_resnet50 -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -v -m spade -n resnet18 -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -v -m stpm -n resnet18 -d mvtec2d -tid 0 -vid 0 -g 1

Semi / -s

python3 main.py -s -m devnet -n net_devnet -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -s -m dra -n net_dra -d mvtec2d -tid 0 -vid 0 -g 1

Fewshot / -f

python3 main.py -f -fe 1 -m patchcore -n wide_resnet50 -d mvtec2d -tid 0 -vid 0 -sp 0.1 -g 1 -fda -fnd 4 -fat rotation
python3 main.py -f -fe 1 -m _patchcore -n wide_resnet50 -d mvtec2d -tid 0 -vid 0 -sp 1 -fda -fnd 4 -g 1
python3 main.py -f -fe 1 -m csflow -n net_csflow -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -f -fe 1 -m cfa -n net_cfa -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -f -fe 1 -m fastflow -n net_fastflow -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -f -fe 1 -m cutpaste -n vit_b_16  -d mvtec2d -tid 0 -vid 0  -g 1
python3 main.py -f -fe 1 -m padim -n resnet18 -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -f -fe 1 -m favae -n net_favae -d mvtec2d -tid 0 -vid 0  -g 1
python3 main.py -f -fe 1 -m cutpaste -n vit_b_16 -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -f -fe 1 -m igd -n net_igd -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -f -fe 1 -m reverse -n net_reverse -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -f -fe 1 -m spade -n resnet18 -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -f -fe 1 -m stpm -n resnet18 -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -f -fe 1 -m simplenet -n wide_resnet50 -d mvtec2d -tid 0 -vid 0 -g 1

Continual / -c

python3 main.py -c -m patchcore -n wide_resnet50 -d mvtec2d -tid 0 1 -vid 0 1 -sp 0.001 -g 1
python3 main.py -c -m csflow -n net_csflow -d mvtec2d -tid 0 1 -vid 0 1 -g 1
python3 main.py -c -m cfa -n net_cfa -d mvtec2d -tid 0 1 -vid 0 1 -g 1
python3 main.py -c -m fastflow -n net_fastflow -d mvtec2d -tid 0 1 -vid 0 1 -g 1
python3 main.py -c -m cutpaste -n vit_b_16  -d mvtec2d -tid 0 1 -vid 0 1  -g 1
python3 main.py -c -m padim -n resnet18 -d mvtec2d -tid 0 1 -vid 0 1 -g 1
python3 main.py -c -m favae -n net_favae -d mvtec2d -tid 0 1 -vid 0 1  -g 1
python3 main.py -c -m cutpaste -n vit_b_16 -d mvtec2d -tid 0 1 -vid 0 1 -g 1
python3 main.py -c -m igd -n net_igd -d mvtec2d -tid 0 1 -vid 0 1 -g 1
python3 main.py -c -m reverse -n net_reverse -d mvtec2d -tid 0 1 -vid 0 1 -g 1
python3 main.py -c -m spade -n resnet18 -d mvtec2d -tid 0 1 -vid 0 1 -g 1
python3 main.py -c -m stpm -n resnet18 -d mvtec2d -tid 0 1 -vid 0 1 -g 1
python3 main.py -c -m dne -n vit_b_16 -d mvtec2d -tid 0 1 -vid 0 1 -g 1
python3 main.py -c -m simplenet -n wide_resnet50 -d mvtec2d -tid 0 1 -vid 0 1 -g 1

Noisy / -z

python3 main.py -z -nr 0.1 -no -m softpatch -n wide_resnet50  -d mvtec2d -tid 0 -vid 0 -sp 0.001 -g 1
python3 main.py -z -nr 0.1 -no -m patchcore -n wide_resnet50  -d mvtec2d  -tid 0 -vid 0 -sp 0.001 -g 1
python3 main.py -z -nr 0.1 -no -m csflow -n net_csflow -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -z -nr 0.1 -no -m cfa -n net_cfa -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -z -nr 0.1 -no -m fastflow -n net_fastflow -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -z -nr 0.1 -no -m cutpaste -n vit_b_16  -d mvtec2d -tid 0 -vid 0  -g 1
python3 main.py -z -nr 0.1 -no -m padim -n resnet18 -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -z -nr 0.1 -no -m favae -n net_favae -d mvtec2d -tid 0 -vid 0  -g 1
python3 main.py -z -nr 0.1 -no -m cutpaste -n vit_b_16 -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -z -nr 0.1 -no -m reverse -n net_reverse -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -z -nr 0.1 -no -m spade -n resnet18 -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -z -nr 0.1 -no -m stpm -n resnet18 -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -z -nr 0.1 -no -m igd -n net_igd -d mvtec2d -tid 0 -vid 0 -g 1
python3 main.py -z -nr 0.1 -no -m simplenet -n wide_resnet50  -d mvtec2d -tid 0 -vid 0 -g 1

Transfer / -t

python3 main.py -t -ttn 8 -m reverse -n net_reverse -d coad -tid 0 -vid 1 -g 1 -ne 10
python3 main.py -t -ttn 8 -m cfa -n net_cfa -d coad -tid 0 -vid 1 -g 1 -ne 10
python3 main.py -t -ttn 8 -m csflow -n net_csflow -d coad -tid 0 -vid 1 -g 1 -ne 10
python3 main.py -t -ttn 8 -m fastflow -n net_fastflow -d coad -tid 0 -vid 1 -g 1 -ne 10
python3 main.py -t -ttn 8 -m favae -n net_favae -d coad -tid 0 -vid 1 -g 1 -ne 10
python3 main.py -t -ttn 8 -m padim -n resnet18 -d coad -tid 0 -vid 1 -g 1
python3 main.py -t -ttn 8 -m patchcore -n wide_resnet50 -d coad -tid 0 -vid 1 -g 1
python3 main.py -t -ttn 8 -m stpm -n resnet18 -d coad -tid 0 -vid 1 -g 1
python3 main.py -t -ttn 8 -m simplenet -n wide_resnet50 -d coad -tid 0 -vid 1 -g 1

Tutorial

How to implement your own methods or datasets, i.e, integrating new methods into the open-iad project?

Please refer to the following steps:

• Register your NEW METHOD (e.g., MODEL, NET, DATASET, SETTING, SERVER) in configuration/registration.py
• Add names of MODEL, NET, DATASET, SETTING into configuration/config.py
• Implement MODEL in arch/_example.py and models/_example/net_example.py
• Put MODEL configuration in configuration/1_model_base/_example.yaml
• Implement DATASET in dataset/_example.py
• Put DATASET configuration in configuration/3_dataset_base/_example.yaml
• Implement NEW SETTING in data_io/data_holder.py
• If provide NEW SETTING, update OUTPUT path of results in tools/record_helper.py
• Add NEW METHOD description in README.md
• Shell command, "python3 main.py -v -m _example -n net_example -d _example -tid 0 -vid 0 -g 1"