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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"