HiAD

<h1 align="center">HiAD: A General High-Resolution Industrial Image Anomaly Detection Framework</h1> <div align="center" style="display: flex; justify-content: center; flex-wrap: wrap;"> <a href='https://arxiv.org/abs/2508.12931'><img src='https://img.shields.io/badge/arXiv-Paper-red'></a>&ensp; <a href='https://pypi.org/project/hiad/'><img src='https://img.shields.io/badge/Pip-Install-green'></a>&ensp; <a href='https://huggingface.co/XimiaoZhang'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HF-Datasets-blue'></a>&ensp; <a href='LICENSE'><img src='https://img.shields.io/badge/License-Apache2.0-yellow'></a>&ensp; <a href='tutorial/quick_start.md'><img src='https://img.shields.io/badge/Tutorial-blue'></a>&ensp; <a href='README_zh.md'><img src='https://img.shields.io/badge/切换为中文-green'></a>&ensp; </div>

Current anomaly detection methods are primarily designed for low-resolution images. However, in modern industrial production, anomalies often appear as subtle and hard-to-detect defects, making them difficult to identify effectively under low-resolution conditions. To address the industry challenge of large images with small defects, we conducted a systematic study focusing on high-resolution industrial image anomaly detection. We thoroughly analyzed the key challenges of this task, established a comprehensive evaluation benchmark, and proposed HiAD, a practical and efficient high-resolution anomaly detection framework. This framework can accurately detect subtle anomalies in images ranging from 1K to 4K resolution, while ensuring fast inference speeds on mainstream consumer-grade GPUs. If you are a researcher in this field, we invite you to read our paper for more technical details.

<div align="center">

| 2048 × 2048 | 4096 × 4096 | | :------------------------------: | :-------------------------------: | | <img src="assets/demo2K.gif" width="330"/> | <img src="assets/demo4K.gif" width="330"/> |

</div>

News

• [09.2025]: Updated DINOv3-based Dinomaly and INP-Former.
• [01.2026]: Updated HiAD v0.2 to support online inference and deployment.

🔧 Installation

$ pip install hiad[cuda11] # for Linux with cuda11 
$ pip install hiad[cuda12] # for Linux with cuda12
$ pip install hiad[cuda]   # for Linux with other cuda versions
$ pip install hiad         # for Windows

_{<em>Since faiss-gpu is not supported on Windows, some features of HiAD may be limited on Windows systems.</em>}

📖 Tutorial

<table> <tr><td align="center"><a href='tutorial/quick_start.md'>Quick Start</a></td><td align="center">Quickly understand how HiAD works through a simple example.</td></tr> <tr><td align="center"><a href='tutorial/advanced.md'>Advanced Settings</a></td><td align="center">Learn about HiAD's advanced features.</td></tr> <tr><td align="center"><a href='tutorial/customized_detectors.md'>Custom Detector</a></td><td align="center">Integrate more anomaly detection algorithms with HiAD.</td></tr> <tr><td align="center"><a href='tutorial/online_inference.md'>Online Inference</a></td><td align="center">Efficient inference and deployment.</td></tr> </table>

🚀 Datasets

| Datasets | <img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" alt="🤗" width="20"/> Hugging Face | ☁️Google Drive | |:------:|:--------:|:-------:| | MVTec-2K | XimiaoZhang/MVTec-2K | MVTec-2K.zip | | VisA-2K | XimiaoZhang/VisA-2K | VisA-2K.zip | | MVTec-4K | XimiaoZhang/MVTec-4K | MVTec-4K.zip |

🌞 Experiments

If you would like to reproduce our experiments, please clone our repository and install:

$ git clone https://github.com/cnulab/HiAD.git
$ cd HiAD
$ pip install -e .[cuda11] # for Linux with cuda11 
$ pip install -e .[cuda12] # for Linux with cuda12
$ pip install -e .[cuda]   # for Linux with other cuda versions
$ pip install -e .         # for Windows

Refer to data/README for dataset preparation.

The experiment scripts are located in the runs directory. Run them using the following command:

# taking PatchCore as an example, for 2 GPUs
python runs/run_patchcore.py --data_root data/MVTec-2K --category bottle --gpus 0,1

💌 Acknowledgement

If you encounter any issues during usage, feel free to open an issue and reach out to us.

If you find it useful, consider giving us a ⭐, we’d really appreciate it!

📌 Citation

@inproceedings{zhang2025towards,
      title={Towards High-Resolution Industrial Image Anomaly Detection}, 
      author={Ximiao Zhang, Min Xu, and Xiuzhuang Zhou},
      year={2025},
      eprint={2508.12931},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}