docs/1_tutorials/4_train.rst

Train & Validation
==================

Training Model
----------------

To train a model, the :class:`~yolo.tools.solver.ModelTrainer` can help manage the training process. Initialize the :class:`~yolo.tools.solver.ModelTrainer` and use the :func:`~yolo.tools.solver.ModelTrainer.solve` function to start the training.

Before starting the training, don't forget to start the progress logger to enable logging the process status. This will also enable `Weights & Biases (wandb) <https://wandb.ai/site>`_ or TensorBoard if configured.

.. code-block:: python

  from yolo import ModelTrainer
  solver = ModelTrainer(cfg, model, converter, progress, device, use_ddp)
  progress.start()
  solver.solve(dataloader)

Training Diagram
~~~~~~~~~~~~~~~~

The following diagram illustrates the training process:

.. mermaid::

  flowchart LR
    subgraph TS["trainer.solve"]
      subgraph TE["train one epoch"]
        subgraph "train one batch"
          backpropagation-->TF[forward]
          TF-->backpropagation
        end
      end
      subgraph validator.solve
          VC["calculate mAP"]-->VF[forward]
          VF[forward]-->VC
      end
    end
    TE-->validator.solve
    validator.solve-->TE

Validation Model
----------------

To validate the model performance, we follow a similar approach as the training process using :class:`~yolo.tools.solver.ModelValidator`.

.. code-block:: python

   from yolo import ModelValidator
   solver = ModelValidator(cfg, model, converter, progress, device, use_ddp)
   progress.start()
   solver.solve(dataloader)

The :class:`~yolo.tools.solver.ModelValidator` class helps manage the validation process, ensuring that the model's performance is evaluated accurately.

.. note:: The original training process already includes the validation phase. Call this separately if you want to run the validation again after the training is completed.