annotations_creators: []
language: en
license: cc-by-4.0
size_categories:
- 1K<n<10K
task_categories:
- image-classification
- image-segmentation
task_ids: []
pretty_name: MVTec AD
tags:
- fiftyone
- image
- image-classification
- image-segmentation
- anomaly-detection
dataset_summary: >
This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 5354
samples.
## Installation
If you haven't already, install FiftyOne:
```bash
pip install -U fiftyone
```
## Usage
```python
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/mvtec-ad")
# Launch the App
session = fo.launch_app(dataset)
```
Dataset Card for MVTec AD
This is a FiftyOne dataset with 5354 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/mvtec-ad")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details
Dataset Description
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.
The data is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).
In particular, it is not allowed to use the dataset for commercial purposes. If you are unsure whether or not your application violates the non-commercial use clause of the license, please contact the dataset's authors.
If you have any questions or comments about the dataset, feel free to contact the dataset's authors via email at re-request@mvtec.com
- Language(s) (NLP): en
- License: cc-by-4.0
Dataset Sources
- Dataset Homepage https://www.mvtec.com/company/research/datasets/mvtec-ad
- Demo: https://try.fiftyone.ai/datasets/mvtec-ad/samples
- Paper: The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection
Dataset Creation
Source Data
Data downloaded and converted from MVTec website
Citation
BibTeX:
@article{Bergmann2021MVTecAnomalyDetection,
title={The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection},
author={Bergmann, Paul and Batzner, Kilian and Fauser, Michael and Sattlegger, David and Steger, Carsten},
journal={International Journal of Computer Vision},
volume={129},
number={4},
pages={1038--1059},
year={2021},
doi={10.1007/s11263-020-01400-4}
}
@inproceedings{Bergmann2019MVTecAD,
title={MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection},
author={Bergmann, Paul and Fauser, Michael and Sattlegger, David and Steger, Carsten},
booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
pages={9584--9592},
year={2019},
doi={10.1109/CVPR.2019.00982}
}