# AHL

**Repository Path**: amos49/AHL

## Basic Information

- **Project Name**: AHL
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-08-14
- **Last Updated**: 2024-08-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# AHL

Official implementation of ["Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection"](https://arxiv.org/pdf/2310.12790.pdf).(accepted by CVPR 2024)

## Setup
- numpy >= 1.22.3
- python >= 3.10.4
- pytorch >= 1.12.1
- torch >= 1.12.1
- torchvision >= 0.13.1
- tqdm >= 4.64.0
- scipy >= 1.10.1
- scikit-image >= 0.19.2
- einops >= 0.6.0

## Run
#### Step 1. Setup the Anomaly Detection dataset

Download the Anomaly Detection Dataset and convert it to MVTec AD format. (For datasets we used in the paper, we provided [the convert script](https://github.com/mala-lab/AHL/tree/main/data).) 
The dataset folder structure should look like:

```
DATA_PATH/
    subset_1/
        train/
            good/
        test/
            good/
            defect_class_1/
            defect_class_2/
            defect_class_3/
            ...
    ...
```
#### Step 2. Running the base model ([DRA](https://github.com/Choubo/DRA/tree/main), [DevNet](https://github.com/Choubo/deviation-network-image)) and save model weights.

#### Step 3. Save [augmentation features](https://github.com/mala-lab/AHL/tree/main/datasets) and multi-scale features extracted from base model's feature extractor. The dataset folder structure of saved features should look like:
```
DATA_PATH/
    subset_1/
        feature/
            train/
            test/
        feature_scale/
            train/
            test/
        aug_dream/
            train/
        aug_dream_scale/
            train/
        aug_mix/
            train/
        aug_mix_scale/
            train/
        aug_paste/
            train/
        aug_paste_scale/
            train/
    ...
```

#### Step 4. Running AHL
```python
python main.py --dataset_root $path-to-dataset --classname $subset-name --feat_classname $subset-name-for-saved-features --experiment_dir $path-to-save-model-weights
```

## Citation

```bibtex
@inproceedings{zhu2024anomaly,
  title={Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection},
  author={Zhu, Jiawen and Ding, Choubo and Tian, Yu and Pang, Guansong},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  year={2024}
}
```