updated readme

README.md

@@ -25,3 +25,51 @@ dataset_info:
   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".