Auto-configuration
==================

Auto-configuration for a deep learning framework means the automatic finding of the most appropriate settings for the training parameters, based on the dataset and the specific task at hand.
Auto-configuration can help to save time, it eases the process of interaction with OpenVINO™ Training Extensions CLI and gives a better baseline for the given dataset.

At this end, we developed a simple auto-configuration functionality to ease the process of training and validation utilizing our framework.
Basically, to start the training and obtain a good baseline with the best trade-off between accuracy and speed we need to pass only a dataset in the right format without specifying anything else:

.. code-block::

    $ otx train --train-data-roots <path_to_data_root>

.. note::

    OpenVINO™ Training Extensions supports also ``otx build`` mode with the auto-configuration feature. We can build OpenVINO™ Training Extensions workspace with the following CLI command:

    .. code-block::

        $ otx build --train-data-roots <path_to_data_root>

Moreover, our dataset can have no train/val splits at all. The Datumaro manager integrated into OpenVINO™ Training Extensions will handle it on its own.
It will recognize the task by analyzing the dataset and if there is no splits for the validation - Datumaro will do a random auto-split, saving this split to the workspace. It could be used with ``otx optimize`` or ``otx train``.

.. note::

    Currently, Datumaro auto-split feature supports 3 formats: `Imagenet <https://www.image-net.org/>`_  (multi-class classification), `COCO <https://cocodataset.org/#format-data>`_ (detection) and `Cityscapes <https://openvinotoolkit.github.io/datumaro/docs/formats/cityscapes/>`_ (semantic segmentation).

After dataset preparation, the training will be started with the middle-sized template to achieve competitive accuracy preserving fast inference.


Supported dataset formats for each task:

- classification: `Imagenet <https://www.image-net.org/>`_, `COCO <https://cocodataset.org/#format-data>`_ (multi-label), :ref:`custom hierarchical <hierarchical_dataset>`
- object detection: `COCO <https://cocodataset.org/#format-data>`_, `Pascal-VOC <https://openvinotoolkit.github.io/datumaro/docs/formats/pascal_voc/>`_, `YOLO <https://openvinotoolkit.github.io/datumaro/docs/formats/yolo/>`_
- semantic segmentation: `Common Semantic Segmentation <https://openvinotoolkit.github.io/datumaro/docs/formats/common_semantic_segmentation/>`_, `Pascal-VOC <https://openvinotoolkit.github.io/datumaro/docs/formats/pascal_voc/>`_, `Cityscapes <https://openvinotoolkit.github.io/datumaro/docs/formats/cityscapes/>`_, `ADE20k <https://openvinotoolkit.github.io/datumaro/docs/formats/ade20k2020/>`_
- action classification: `CVAT <https://opencv.github.io/cvat/docs/manual/advanced/xml_format/>`_
- action detection: `CVAT <https://opencv.github.io/cvat/docs/manual/advanced/xml_format/>`_
- anomaly classification: `MVTec <https://www.mvtec.com/company/research/datasets/mvtec-ad>`_
- anomaly detection: `MVTec <https://www.mvtec.com/company/research/datasets/mvtec-ad>`_
- anomaly segmentation: `MVTec <https://www.mvtec.com/company/research/datasets/mvtec-ad>`_
- instance segmentation: `COCO <https://cocodataset.org/#format-data>`_, `Pascal-VOC <https://openvinotoolkit.github.io/datumaro/docs/formats/pascal_voc/>`_

If we have a dataset format occluded with other tasks, for example ``COCO`` format, we should directly emphasize the task type and use ``otx build`` first with an additional CLI option. If not, OpenVINO™ Training Extensions automatically chooses the task type that you might not intend:

.. code-block::

    $ otx build --train-data-roots <path_to_data_root> 
                --task {CLASSIFICATION, DETECTION, SEGMENTATION, ACTION_CLASSIFICATION, ACTION_DETECTION, ANOMALY_CLASSIFICATION, ANOMALY_DETECTION, ANOMALY_SEGMENTATION, INSTANCE_SEGMENTATION}

It will create a task-specific workspace folder with configured template and auto dataset split if supported.

Move to this folder and simply run without any options to start training:

.. code-block::

    $ otx train