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CVAT#

Format Specification#

CVAT format is a dedicated data format provided by the CVAT.ai annotation tool. It is formatted using XML (eXtensible Markup Language). For detailed information about the XML file specification, please refer to this link. CVAT format includes two data formats for different purposes:

  • CVAT for video: This format is used for video tasks. It treats a track as the atomic unit of an annotation object. Each track corresponds to an object that can be present in multiple frames. Therefore, each annotation is a child of a track and includes a frame ID to indicate the associated time.

  • CVAT for images: This format is used for image tasks. It maintains a list of images. For each image, it includes annotations derived from objects within the image. This format is more similar to other commonly used data formats in computer vision tasks.

Supported annotation types:

  • Bbox

  • Label

  • Points

  • Polygon

  • PolyLine

  • Mask

Supported annotation attributes:

  • It supports any arbitrary boolean, floating number, or string attribute.

Convert CVAT dataset#

A CVAT dataset can be converted in the following way:

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

A CVAT dataset directory should have the following structure:

  • Exported from the CVAT project: If the dataset is exported from the CVAT project, the image files are grouped into subsets and they are located under the images directory.

    └─ Dataset/
    ├── dataset_meta.json # a list of custom labels (optional)
    ├── annotations.xml
    └── images/
        ├── <subset-1>
        │   ├── <image_name1.ext>
        │   ├── <image_name1.ext>
        │   └── ...
        ├── ...
        └── <subset-n>
            ├── <image_name1.ext>
            └── ...

  • Exported from the CVAT task: If the dataset is exported from the CVAT task, there is only one subset in the task. Therefore, there is no subset sub-directory in the images directory.

    └─ Dataset/
    ├── dataset_meta.json # a list of custom labels (optional)
    ├── annotations.xml
    └── images/
        ├── <image_name1.ext>
        ├── <image_name1.ext>
        └── ...

The annotation file must have the name like annotations.xml in the root directory. The image files exist in the images directory. There are sub-directory according to their subset information. However, this is only provided if the dataset is exported from the CVAT project. If the dataset is exported from the CVAT task, all images are directly under the images directory without subset information. To add custom classes, you can use dataset_meta.json.

Export to other formats#

Datumaro can convert CVAT dataset into any other format Datumaro supports. To get the expected result, convert the dataset to formats that support the specified task (e.g. for panoptic segmentation - VOC, CamVID)

There are several ways to convert a CVAT dataset to other dataset formats using CLI:

datum convert --input-format cvat --input-path <path/to/dataset> \
    --output-format voc --output-dir <output/dir>

Or, using Python API:

import datumaro as dm

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

Export to CVAT#

There are several ways to convert a dataset to CVAT format:

# converting to CVAT format from other format
datum convert --input-format voc --input-path <path/to/dataset> \
    --output-format cvat --output-dir <output/dir> -- --save-media

Extra options for exporting to CVAT format:

  • --save-media allow to export dataset with saving media files (by default False)

  • --image-ext IMAGE_EXT allow to specify image extension for exporting dataset (by default - keep original or use .jpg, if none)

  • --save-dataset-meta allow to export dataset with saving dataset meta file (by default False)

  • --reindex assign new indices to frames

  • --allow-undeclared-attrs write annotation attributes even if they are not present in the input dataset metainfo

When performing convert to CVAT format, you may encounter a warning message like the following:

skipping undeclared attribute 'is_crowd' for label '<label>' (allow with --allow-undeclared-attrs option)

In such cases, you can bypass this warning by using the --allow-undeclared-attrs option as follows:

datum convert --input-format <source-format> --input-path <path/to/dataset> \
    --output-format cvat --output-dir <output/dir> -- --allow-undeclared-attrs

This allows you to proceed with the export while bypassing the warning.

Examples#

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

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