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license: cc-by-nc-sa-4.0
language:
- en
size_categories:
- 100B<n<1T
---
# ReinAD: Towards Real-world Industrial Anomaly Detection with a Comprehensive Contrastive Dataset
Our dataset consists of a training set and a test set. All normal and anomaly images are in hdf5 format. In the mask annotations, pixels with a value of 0 represent normal regions, and pixels with a value of 1 represent anomaly regions.
The file structure of the training set and the test set are consistent, as follows:
```text
dataset/
├── train/
│ ├── category1.h5
│ ├── category2.h5
│ └── ...
│
└── test/
├── category1.h5
├── category2.h5
└── ...
```
The structure of the hdf5 file is as follows, where ```chunk_size = 100```:
```text
/ (root)
├── attrs
│ ├── split: "train"/"test"
│ └── category: category_name
│
├── Images
│ ├── Anomaly_0: [chunk_size, H, W, C] # Anomaly images
│ ├── Anomaly_1: [chunk_size, H, W, C]
│ ├── ...
│ ├── Normal_0: [chunk_size, H, W, C] # Normal images
│ ├── Normal_1: [chunk_size, H, W, C]
│ └── ...
│
└── Masks
├── Anomaly_0: [chunk_size, H, W] # Pixel-level annotations for anomaly images
├── Anomaly_1: [chunk_size, H, W]
└── ...
``` |