  Deep learning requires large amounts of data, and a well-defined pipeline for
labeling and augmentation. Current solutions support numerous computer vision
tasks with dedicated annotation types and formats, such as bounding boxes,
polygons, and key points. These annotations can be combined into a single data
format to benefit approaches such as multi-task models. However, to our
knowledge, no available labeling tool supports the export functionality for a
combined benchmark format, and no augmentation library supports transformations
for the combination of all. In this work, these functionalities are presented,
with visual data annotation and augmentation to train a multi-task model
(object detection, segmentation, and key point extraction). The tools are
demonstrated in two robot perception use cases.
