# VoTT JSON ## Format specification [VoTT](https://github.com/microsoft/VoTT) (Visual Object Tagging Tool) is an open source annotation tool released by Microsoft. [VoTT JSON](https://roboflow.com/formats/vott-json) is the format used by VoTT when the user exports a project and selects "VoTT JSON" as the export format. Supported annotation types: - `Bbox` ## Import VoTT dataset A Datumaro project with a VoTT JSON source can be created in the following way: ```bash datum project create datum project import --format vott_json ``` It is also possible to import the dataset using Python API: ```python import datumaro as dm vott_json_dataset = dm.Dataset.import_from('', 'vott_json') ``` VoTT JSON dataset directory should have the following structure: ``` dataset/ ├── dataset_meta.json # a list of custom labels (optional) ├── img0001.jpg ├── img0002.jpg ├── img0003.jpg ├── img0004.jpg ├── ... ├── test-export.json ├── train-export.json └── ... ``` To add custom classes, you can use [`dataset_meta.json`](/docs/data-formats/formats/index.rst#dataset-meta-info-file). ## Export to other formats Datumaro can convert a VoTT JSON dataset into any other format [Datumaro supports](/docs/data-formats/formats/index.rst). To get the expected result, convert the dataset to a format that supports bounding boxes. There are several ways to convert a VoTT JSON dataset to other dataset formats using CLI: ```bash datum project create datum project import -f vott_json datum project export -f voc -o ./save_dir -- --save-media ``` or ``` bash datum convert -if vott_json -i \ -f voc -o -- --save-media ``` Or, using Python API: ```python import datumaro as dm dataset = dm.Dataset.import_from('', 'vott_json') dataset.export('save_dir', 'voc') ``` ## Examples Examples of using this format from the code can be found in [VoTT JSON tests](https://github.com/openvinotoolkit/datumaro/blob/develop/tests/unit/test_vott_json_format.py).