Model Training and Testing
--------------------------

.. role:: guimenu

.. role:: guiaction

.. role:: guioption

In the :guimenu:`Train` section, you can train deep learning models for various
AI tasks (e.g., object detection) using generated datasets.


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.. note::
  See :ref:`Tutorial: Train Object Detection Model <using-the-train-tab-method-neura-dlis1>`

Model Training
~~~~~~~~~~~~~~~~~~~~~~~~

.. figure:: /_static/ai_hub/train/ai-hub-train.png
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Training parameters
++++++++++++++++++++++++++++

The training interface allows you to configure the following parameters:

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    * - Parameter
      - Description

    * - **Type of training**
      - Select the task for training

        - Object Detection (detecting and localizing objects in an image)

    * - **Method**
      - Choose an available method (e.g., `neura_DLIS1`)
        for the selected task. The method determines the algorithm and
        architecture used for training.

    * - **Dataset list**
      - Choose the dataset(s) for training from available datasets.

    * - **Dataset type**
      - Choose the dataset type (e.g., `real`, `synthetic` etc.)

    * - **Name of model**
      - Provide a custom name for your model (e.g., `my_first_model`).

    * - **Number of Iterations**
      - Define the total number of training steps using the slider (e.g.,
        **500** iterations for small datasets of ~30 images, **2000**
        iterations for medium datasets [~200 images]).


Advanced Training parameters
++++++++++++++++++++++++++++
Click on :guimenu:`Edit` to access and adjust advanced hyperparameters for
model training.

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    * - Parameter
      - Description

    * - **Learning rate**
      - Set the learning rate for training, which controls how much the model
        adjusts weights with each iteration.

    * - **Checkpoint saving after n iterations**
      - Define the interval (in iterations) at which the training process saves
        checkpoints, enabling recovery and resuming from saved states.

    * - **Pretrained model**
      - Choose a pretrained model as the starting point for training.

    * - **Batch size**
      - Specify the number of samples processed together during a single training step.

    * - **Warmup iterations**
      - Define the number of iterations for a warmup phase, during which the
        learning rate gradually increases to its configured value.

    * - **Empty images**
      - Toggle the use of empty images during training. This option can help
        the model learn to handle cases with no objects detected in an image.

.. figure:: /_static/ai_hub/train/ai-hub-train-advanced.png
    :align: center
    :class: padded-image

Best Practices
++++++++++++++

* Ensure **clean, diverse, and representative datasets** to enhance model
  performance and generalization.

  * Different lighting conditions and varied backgrounds
  * Vary object positions and camera angles

* When training a model with multiple objects, provide approximately the **same amount of data for each object** to maintain balance and prevent bias.
* Incorporate **both synthetic and real datasets** to improve model robustness and adaptability to real-world scenarios.
* Include data about the **actual use-case environment**.

Model Testing
~~~~~~~~~~~~~
A trained model's qualitative performance can be evaluated by making live
inferences. Click on :guimenu:`Test model` to choose the model to be tested on
the live camera feed.

.. figure:: /_static/ai_hub/train/ai-hub-test.png
    :align: center
    :class: padded-image