Duplicate from Voxel51/mvtec-ad

Co-authored-by: Jacob Marks <jamarks@users.noreply.huggingface.co>

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README.md

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+---
+annotations_creators: []
+language: en
+license: cc-by-nc-sa-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: >
+
+
+
+  ![image/png](dataset_preview.jpg)
+
+
+
+  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
+
+<!-- Provide a quick summary of the dataset. -->
+
+
+
+
+![image/png](dataset_preview.jpg)
+
+This dataset originates from MVTec but is provided in a different format. You can easily load it using [FiftyOne](https://github.com/voxel51/fiftyone) 
+The total number of samples remains the same as the original: 5,354.
+
+## 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 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-NC-SA 4.0
+
+### Dataset Sources
+
+<!-- Provide the basic links for the dataset. -->
+
+- **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](https://link.springer.com/content/pdf/10.1007/s11263-020-01400-4.pdf)
+
+
+## Dataset Creation
+
+
+### Source Data
+
+Data downloaded and converted from [MVTec website](https://www.mvtec.com/company/research/datasets/mvtec-ad)
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+```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}
+}
+```
+
+## Dataset Card Authors
+
+[Jacob Marks](https://huggingface.co/jamarks)