Difference model.train() and DetectionTrainer.train()

[noel077]

Hi community,

I am refering to the docs (https://docs.ultralytics.com/usage/python/).

In the section "Train" the training is accoomplished in a simple way:
model = YOLO('yolov8n.pt') # pass any model type
model.train(epochs=5)

Whereas in the section "Using Trainers" the training is done in a completely different manner:
trainer = DetectionTrainer(overrides={})
trainer.train()
trained_model = trainer.best

Especially when it comes to the validation, I don't really understand the two different approaches. For the first one, we can simply use:
model.val()

whereas for the second approach it is necessary to create a new Validator:
val = DetectionValidator(args=...)
val(model=trained_model)

Looking at the source code, there is a def val in ultralytics.yolo.v8.detect.val.py but also a def val in ultralytics.yolo.enginge.model.py.

Furhter there is a similar def validate in ultalytics.yolo.engine.trainer.py.

I am getting really confused why there are several functions for validation.

I know this is a pretty basic question, but I would appreciate if someone could help me! Especially I am wondering what is the best approach in my use case. I want to perform the training with several parameters for which I will pass a custom cfg.yaml file.
Afterwards I want to perform a validation and obtain the bounding box loss.

Thank you in advance!

[glenn-jocher]

@noel077 hello there!

The difference between model.train() and DetectionTrainer.train() lies in the level of customization and control you have over the training process.

Using model.train() is a straightforward way to start training a model with default settings. It's a high-level approach that abstracts away many of the underlying details, making it easier for users to quickly train a model without needing to configure many parameters.

On the other hand, DetectionTrainer.train() provides a more granular level of control. It allows you to customize the training process by passing a dictionary of overrides that can modify various aspects of the training configuration. This is useful when you need to experiment with different hyperparameters or when you have specific requirements for your training setup.

Regarding validation, model.val() is a convenient method to validate the model using the high-level API, which is consistent with the simplicity of model.train(). The DetectionValidator class, used in conjunction with DetectionTrainer, offers a more detailed and customizable validation process, which might be necessary if you're doing more in-depth experimentation or analysis.

The presence of multiple validation functions in the source code reflects the different layers of abstraction provided by the API. Each serves a different purpose depending on whether you're using the high-level or low-level API.

For your use case, where you want to train with several parameters and a custom cfg.yaml file, and then perform validation to obtain the bounding box loss, you might find the DetectionTrainer and DetectionValidator classes more suitable. They will give you the flexibility to fine-tune the training and validation processes according to your specific needs.

Remember, there's no one-size-fits-all answer; the best approach depends on your familiarity with the framework, your specific requirements, and how much control you need over the training and validation processes. 🚀

Happy training!