ADNet

ADNet is a large-scale, multi-domain benchmark for visual anomaly detection and localization. It contains 196,294 RGB images from 380 real-world categories across five application domains: Electronics, Industry, Agrifood, Infrastructure, and Medical.

ADNet standardizes data collected from 49 publicly available anomaly-detection datasets into a unified MVTec-style structure. The benchmark supports normal-only training, image-level anomaly detection, pixel-level anomaly localization, and large-scale multi-class anomaly-detection research.

For further details, see the paper:

ADNet: A Large-Scale and Extensible Multi-Domain Benchmark for Anomaly Detection Across 380 Real-World Categories
Hai Ling, Jia Guo, Zhulin Tao, Yunkang Cao, Donglin Di, Hongyan Xu, Xiu Su, Yang Song, and Lei Fan.
arXiv:2511.20169

Access and Licensing

ADNet is released for academic research purposes only.

The dataset is distributed under the CC BY-NC 4.0 License (Attribution-NonCommercial 4.0 International). Commercial use is prohibited.

To request access, please apply using an institutional email address, such as an .edu or .ac.cn address.

• Requests from personal email addresses or commercial domains may be rejected.
• Please clearly state your institution and intended research use in the application form.
• Access requests are reviewed manually. Please allow 1–2 working days for approval.

Dataset Overview

ADNet contains:

• 380 object categories
• 5 application domains
• 196,294 RGB images
• 116,192 normal training images
• 80,102 test images
• 19,791 normal test images
• 60,311 anomalous test images
• 60,311 corresponding pixel-level anomaly masks

The dataset is organized into five domain-level archives rather than separate archives for every category:

• Agrifood.zip
• Electronics.zip
• Industry.zip
• Infrastructure.zip
• Medical.zip

Dataset Statistics

Domain	Categories	Normal training images	Normal test images	Anomalous test images	Total images
Agrifood	69	19,504	2,860	13,041	35,405
Electronics	77	22,886	3,989	15,105	41,980
Industry	154	44,278	7,680	20,983	72,941
Infrastructure	52	18,732	3,297	6,810	28,839
Medical	28	10,792	1,965	4,372	17,129
Total	380	116,192	19,791	60,311	196,294

Training sets contain only anomaly-free images. Each test set contains both normal and anomalous images, with pixel-level masks provided for anomalous samples.

Dataset Structure

After extraction, ADNet follows a domain-category hierarchy with a unified MVTec-style organization:

ADNet/
├── Agrifood/
│   └── <category>/
├── Electronics/
│   └── <category>/
├── Industry/
│   └── <category>/
├── Infrastructure/
│   └── <category>/
└── Medical/
    └── <category>/

Each category uses the following structure:

<domain>/
└── <category>/
    ├── train/
    │   └── good/
    │       └── <normal training images>
    ├── test/
    │   ├── good/
    │   │   └── <normal test images>
    │   └── <defect_type>/
    │       └── <anomalous test images>
    └── ground_truth/
        └── <defect_type>/
            └── <pixel-level anomaly masks>

A concrete example is:

ADNet/
└── Industry/
    └── screw_bag/
        ├── train/
        │   └── good/
        │       ├── 026.png
        │       ├── 077.png
        │       └── 078.png
        ├── test/
        │   ├── good/
        │   ├── logical_anomalies/
        │   │   └── 007.png
        │   └── structural_anomalies/
        │       └── 058.png
        └── ground_truth/
            ├── logical_anomalies/
            │   └── 007.png
            └── structural_anomalies/
                └── 058.png

For an anomalous test image, the corresponding mask has the same defect-type directory and filename:

test/<defect_type>/<filename>
ground_truth/<defect_type>/<filename>

Normal training and test images do not have corresponding anomaly masks.

Image extensions may vary across the source datasets. Users should not assume that every image is a .png file or that mask filenames contain a _mask suffix.

Supported Tasks

ADNet supports the following primary tasks:

• Image-level anomaly detection: determine whether a test image is normal or anomalous.
• Pixel-level anomaly localization: identify anomalous regions using the provided segmentation masks.
• One-for-one anomaly detection: train and evaluate one model for each category.
• Multi-class anomaly detection: jointly train a unified model using multiple categories or all 380 categories.
• Cross-domain anomaly detection: study transfer and generalization across the five application domains.