Annotating an image-based dataset can be quite tedious and time-consuming, which is especially true for segmentation tasks. When labelling, it can take 10 times longer to draw a good polygon around an object than a bounding box. The Auto-Label service of TAO Data Servcies is designed to reduce the time spent annotating an image dataset. Currently, this service focuses on automatically generating instance segmentation masks given the groundtruth bounding boxes.
The Auto-Label service expects that the groundtruth annotation of a directory of images is stored in a COCO-format JSON file.
Refer to the Mask Auto Labeler (MAL) documentation for more information about creating a spec file.
The Auto-Label service supports the following tasks:
generate- Generates pseudo-labels based on the input bounding boxes
The Auto-Label service can be invoked from the TAO Toolkit Launcher using the following convention on the command-line:
tao dataset auto-label generate [-h] -e <experiment spec>
[-r <results_dir>]
[--gpus <num_gpus>]
Required Arguments
-e, --experiment_spec_file: The experiment specification file
Optional Arguments
--gpus: The number of GPUs to use for inference. The default value is 1.-h, --help: Show this help message and exit.
Here’s an example of using the Auto-Label generate command with an MAL model:
tao dataset auto-label generate --gpus 2 -e /path/to/spec.yaml