This is the code demo for the paper:
Dong, Y., Lu, X., Li, R., Song, W., van Arem, B. and Farah, H., 2023. Intelligent Anomaly Detection for Lane Rendering Using Transformer with Self-Supervised Pre-Training and Customized Fine-Tuning. arXiv preprint arXiv:2312.04398.

Prepare environment

pytorch version: 1.8.1+cu111

apt -y install python3-tk

pip install timm==0.3.2
pip install einops

Dataset

https://drive.google.com/file/d/1qULO6dt0rjHTZcXP62FT4cqPq60XmkDe/view?usp=sharing

Trian

file tree

├── UM-MAE
│   ├── DET
│   │   └── configs
│   ├── IN1K
│   ├── SEG
│   │   ├── configs
│   │   └── mmcv_custom
│   ├── __pycache__
│   ├── figs
│   ├── util
│   │   └── __pycache__
│   ├── visual
│   └── work_dirs
│       └── allimage
└── huaweidata
    ├── pretrain-allimage
    │   └── all
    ├── test_images
    └── train_image

Pretrain

Use all the test data and train data(without labels) to pre-train the model

cd UM-MAE
make pretrain

Treat it as a 2-class classification problem

Categorize the dataset into two categories, 0,1, with 0 representing normal image data and 1 representing abnormal instance. then run the following code

make finetunec2

make testc2

Treat it as an 8-class classification problem

Categorize the dataset into 0-7, in total 8 classes, each representing one type of data, if the image has multiple defects, it will be placed into multiple classes of data

make finetunec8

make testc8

Treat it as a 9-class classification problem

Categorize the dataset into 0-8, 9 categories, data with each abnormal image instance (regardless of what type of anomaly is ) will be placed in the ninth category

make finetune9

make testc9