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Advanced Customization

Both the Ultralytics YOLO command-line and Python interfaces are simply a high-level abstraction on the base engine executors. Let's take a look at the Trainer engine.

Watch: Mastering Ultralytics YOLOv8: Advanced Customization


BaseTrainer contains the generic boilerplate training routine. It can be customized for any task based over overriding the required functions or operations as long the as correct formats are followed. For example, you can support your own custom model and dataloader by just overriding these functions:

  • get_model(cfg, weights) - The function that builds the model to be trained
  • get_dataloader() - The function that builds the dataloader More details and source code can be found in BaseTrainer Reference


Here's how you can use the YOLOv8 DetectionTrainer and customize it.

from ultralytics.models.yolo.detect import DetectionTrainer

trainer = DetectionTrainer(overrides={...})
trained_model =  # get best model

Customizing the DetectionTrainer

Let's customize the trainer to train a custom detection model that is not supported directly. You can do this by simply overloading the existing the get_model functionality:

from ultralytics.models.yolo.detect import DetectionTrainer

class CustomTrainer(DetectionTrainer):
    def get_model(self, cfg, weights):
        """Loads a custom detection model given configuration and weight files."""

trainer = CustomTrainer(overrides={...})

You now realize that you need to customize the trainer further to:

  • Customize the loss function.
  • Add callback that uploads model to your Google Drive after every 10 epochs Here's how you can do it:
from ultralytics.models.yolo.detect import DetectionTrainer
from ultralytics.nn.tasks import DetectionModel

class MyCustomModel(DetectionModel):
    def init_criterion(self):
        """Initializes the loss function and adds a callback for uploading the model to Google Drive every 10 epochs."""

class CustomTrainer(DetectionTrainer):
    def get_model(self, cfg, weights):
        """Returns a customized detection model instance configured with specified config and weights."""
        return MyCustomModel(...)

# callback to upload model weights
def log_model(trainer):
    """Logs the path of the last model weight used by the trainer."""
    last_weight_path = trainer.last

trainer = CustomTrainer(overrides={...})
trainer.add_callback("on_train_epoch_end", log_model)  # Adds to existing callback

To know more about Callback triggering events and entry point, checkout our Callbacks Guide

Other engine components

There are other components that can be customized similarly like Validators and Predictors. See Reference section for more information on these.

Created 2023-11-12, Updated 2024-06-02
Authors: glenn-jocher (6), RizwanMunawar (1), AyushExel (1), Laughing-q (1)