README.md

---
license: cc-by-nc-sa-4.0
task_categories:
- image-classification
- image-segmentation
dataset_info:
  features:
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': normal
          '1': abnormal
  splits:
  - name: train
    num_bytes: 252483624
    num_examples: 219
  - name: test
    num_bytes: 26466712
    num_examples: 132
  download_size: 404252480
  dataset_size: 278950336
---
## MVTec Capsule Category

### Dataset Labels

```
{0: "normal", 1: "abnormal"}
```


### Number of Images

```json
{'train': 219, 'test': 132}
```


### How to Use

- Install [datasets](https://pypi.org/project/datasets/):

```bash
pip install datasets
```

- Load the dataset:

```python
from datasets import load_dataset
ds = load_dataset("alexsu52/mvtec_capsule")
example = ds['train'][0]
```

### MVTEC Dataset Page
[https://www.mvtec.com/company/research/datasets/mvtec-ad](https://www.mvtec.com/company/research/datasets/mvtec-ad)

### Citation

Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, Carsten Steger: The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection; in: International Journal of Computer Vision 129(4):1038-1059, 2021, DOI: 10.1007/s11263-020-01400-4.

Paul Bergmann, Michael Fauser, David Sattlegger, Carsten Steger: MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection; in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9584-9592, 2019, DOI: 10.1109/CVPR.2019.00982.

### License
CC BY-NC-SA 4.0

### Dataset Summary
MVTec AD is a dataset for benchmarking anomaly detection methods with a focus on industrial inspection. It contains over 5000 high-resolution images divided into fifteen different object and texture categories. Each category comprises a set of defect-free training images and a test set of images with various kinds of defects as well as images without defects.

Pixel-precise annotations of all anomalies are also provided. More information can be in our paper "MVTec AD – A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection" and its extended version "The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection".