Dinomaly

PyTorch Implementation of "Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection".

The code is preview version, so it could be really ugly with minor errors. We will try to reformat it after the paper is accepted.

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1. Environments

Create a new conda environment and install required packages.

conda create -n my_env python=3.8.12
conda activate my_env
pip install -r requirements.txt

Experiments are conducted on NVIDIA GeForce RTX 3090 (24GB). Same GPU and package version are recommended.

2. Prepare Datasets

Noted that ../ is the upper directory of Dinomaly code. It is where we keep all the datasets by default. You can also alter it according to your need, just remember to modify the data_path in the code.

MVTec AD

Download the MVTec-AD dataset from URL. Unzip the file to ../mvtec_anomaly_detection.

|-- mvtec_anomaly_detection
    |-- bottle
    |-- cable
    |-- capsule
    |-- ....

VisA

Download the VisA dataset from URL. Unzip the file to ../VisA/. Preprocess the dataset to ../VisA_pytorch/ in 1-class mode by their official splitting code.

You can also run the following command for preprocess, which is the same to their official code.

python ./prepare_data/prepare_visa.py --split-type 1cls --data-folder ../VisA --save-folder ../VisA_pytorch --split-file ./prepare_data/split_csv/1cls.csv

../VisA_pytorch will be like:

|-- VisA_pytorch
    |-- 1cls
        |-- candle
            |-- ground_truth
            |-- test
                    |-- good
                    |-- bad
            |-- train
                    |-- good
        |-- capsules
        |-- ....

Real-IAD

Contact the authors of Real-IAD URL to get the net disk link.

Download and unzip realiad_1024 and realiad_jsons in ../Real-IAD. ../Real-IAD will be like:

|-- Real-IAD
    |-- realiad_1024
        |-- audiokack
        |-- bottle_cap
        |-- ....
    |-- realiad_jsons
        |-- realiad_jsons
        |-- realiad_jsons_sv
        |-- realiad_jsons_fuiad_0.0
        |-- ....

3. Run Experiments

Multi-Class Setting

python dinomaly_mvtec_uni.py --data_path ../mvtec_anomaly_detection

python dinomaly_visa_uni.py --data_path ../VisA_pytorch/1cls

python dinomaly_realiad_uni.py --data_path ../Real-IAD

Conventional Class-Separted Setting

python dinomaly_mvtec_sep.py --data_path ../mvtec_anomaly_detection

python dinomaly_visa_sep.py --data_path ../VisA_pytorch/1cls

python dinomaly_realiad_sep.py --data_path ../Real-IAD

Training Unstability: The optimization can be unstable with loss spikes (e.g. ...0.05, 0.04, 0.04, 0.32, 0.23, 0.08...) , which can be harmful to performance. This occurs very very rare. If you see such loss spikes during training, consider change a random seed.

guojiajeremy / Dinomaly