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87 lines
2.8 KiB
87 lines
2.8 KiB
---
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comments: true
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description: Learn how to train and customize your models fast with the Ultralytics YOLO 'DetectionTrainer' and 'CustomTrainer'. Read more here!
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keywords: Ultralytics, YOLO, DetectionTrainer, BaseTrainer, engine components, trainers, customizing, callbacks, validators, predictors
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---
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Both the Ultralytics YOLO command-line and python interfaces are simply a high-level abstraction on the base engine
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executors. Let's take a look at the Trainer engine.
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## BaseTrainer
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BaseTrainer contains the generic boilerplate training routine. It can be customized for any task based over overriding
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the required functions or operations as long the as correct formats are followed. For example, you can support your own
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custom model and dataloader by just overriding these functions:
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* `get_model(cfg, weights)` - The function that builds the model to be trained
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* `get_dataloder()` - The function that builds the dataloader
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More details and source code can be found in [`BaseTrainer` Reference](../reference/engine/trainer.md)
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## DetectionTrainer
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Here's how you can use the YOLOv8 `DetectionTrainer` and customize it.
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```python
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from ultralytics.models.yolo.detect import DetectionTrainer
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trainer = DetectionTrainer(overrides={...})
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trainer.train()
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trained_model = trainer.best # get best model
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```
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### Customizing the DetectionTrainer
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Let's customize the trainer **to train a custom detection model** that is not supported directly. You can do this by
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simply overloading the existing the `get_model` functionality:
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```python
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from ultralytics.models.yolo.detect import DetectionTrainer
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class CustomTrainer(DetectionTrainer):
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def get_model(self, cfg, weights):
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...
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trainer = CustomTrainer(overrides={...})
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trainer.train()
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```
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You now realize that you need to customize the trainer further to:
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* Customize the `loss function`.
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* Add `callback` that uploads model to your Google Drive after every 10 `epochs`
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Here's how you can do it:
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```python
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from ultralytics.models.yolo.detect import DetectionTrainer
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from ultralytics.nn.tasks import DetectionModel
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class MyCustomModel(DetectionModel):
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def init_criterion(self):
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...
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class CustomTrainer(DetectionTrainer):
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def get_model(self, cfg, weights):
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return MyCustomModel(...)
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# callback to upload model weights
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def log_model(trainer):
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last_weight_path = trainer.last
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...
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trainer = CustomTrainer(overrides={...})
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trainer.add_callback("on_train_epoch_end", log_model) # Adds to existing callback
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trainer.train()
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```
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To know more about Callback triggering events and entry point, checkout our [Callbacks Guide](callbacks.md)
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## Other engine components
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There are other components that can be customized similarly like `Validators` and `Predictors`
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See Reference section for more information on these. |