MVTec AD#

Format specification#

The MVTec AD format specification is available here.

The dataset has annotations for detecting abnormal pixels through binary masks and it turns into bounding boxes or abnormal labels for supporting classification, detection, and segmentation tasks. The MVTec AD dataset is composed of training data only for good category without any annotation and testing data for both good and multiple defective categories with masks. The dataset contains total 15 kinds of objects or textures.

Supported tasks / formats:

The combined format - mvtec
Image classification - mvtec_classification
Object detection - mvtec_detection
Instance segmentation - mvtec_segmentation

Supported annotation types:

Label (classification)
Bbox (detection)
Mask (segmentation)

Convert MVTec AD dataset#

The MVTec AD dataset is available for free download here.

A MVTec AD dataset can be converted in the following way:

datum convert --input-format mvtec_segmentation --input-path <path/to/dataset> \
    --output-format <desired_format> --output-dir <output/dir>

The MVTec AD dataset directory should have the following structure:

└─ Dataset/Category
   ├── train/
   │   ├── good/ # directory with list of good images
   │   │   ├── img1.png
   │   |   ├── img2.png
   │   |   └── ...
   ├── test/
   │   ├── good/ # directory with list of good images
   │   │   ├── img1.png
   │   |   ├── img2.png
   │   |   └── ...
   │   ├── defective1/ # directory with list of defective images
   │   │   ├── img1.png
   │   |   ├── img2.png
   │   |   └── ...
   │   ├── defective2/ # directory with list of defective images
   │   │   ├── img1.png
   │   |   ├── img2.png
   │   |   └── ...
   └── ground_truth/ # directory with semantic segmentation masks
       ├── defective1/ # directory with list of defective images for detection and segmentation task
       │   ├── img1_mask.png
       |   ├── img2_mask.png
       |   └── ...
       ├── defective2/ # directory with list of defective images for detection and segmentation task
       │   ├── img1_mask.png
       |   ├── img2_mask.png
       |   └── ...

Export to other formats#

Datumaro can convert a MVTec AD dataset into any other format Datumaro supports.

Such conversion will only be successful if the output format can represent the type of dataset you want to convert, e.g., image classification annotations can be saved in ImageNet format, but not as COCO keypoints.

There are several ways to convert a MVTec AD dataset to other dataset formats:

datum convert --input-format mvtec --input-path <path/to/mvtec> \
    --output-format coco --output-dir <output/dir>

Or, using Python API:

import datumaro as dm

dataset = dm.Dataset.import_from('<path/to/mvtec>', 'mvtec')
dataset.export('save_dir', 'coco', save_media=True)

Export to MVTec AD format#

There are several ways to convert an existing dataset to MVTec AD format:

# converting to MVTec AD format from other format
datum convert --input-format imagenet --input-path <path/to/dataset> \
    --output-format mvtec --output-dir <output/dir> \
    -- \
    --save-media

Extra options for exporting to MVTec AD format:

--save-media - allow to export dataset with saving media files (by default False)
--tasks TASKS - allow to specify tasks for export dataset, by default Datumaro uses all tasks.

datum convert --input-format <source-format> --input-path <path/to/dataset> \
    --output-format mvtec --output-dir <output/dir> -- --tasks detection,classification

Examples#

Examples of using this format from the code can be found in the format tests.