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YOLO11 Tasks, Modes, Usage, Macros and Solutions Updates (#16593)

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docs/en/usage/engine.md
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@ -1,7 +1,7 @@
---
comments: true
description: Learn to customize the YOLOv8 Trainer for specific tasks. Step-by-step instructions with Python examples for maximum model performance.
keywords: Ultralytics, YOLOv8, Trainer Customization, Python, Machine Learning, AI, Model Training, DetectionTrainer, Custom Models
description: Learn to customize the YOLO11 Trainer for specific tasks. Step-by-step instructions with Python examples for maximum model performance.
keywords: Ultralytics, YOLO11, Trainer Customization, Python, Machine Learning, AI, Model Training, DetectionTrainer, Custom Models
---
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.
@ -14,7 +14,7 @@ Both the Ultralytics YOLO command-line and Python interfaces are simply a high-l
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Mastering Ultralytics YOLOv8: Advanced Customization
<strong>Watch:</strong> Mastering Ultralytics YOLO: Advanced Customization
</p>
## BaseTrainer
@ -26,7 +26,7 @@ BaseTrainer contains the generic boilerplate training routine. It can be customi
## DetectionTrainer
Here's how you can use the YOLOv8 `DetectionTrainer` and customize it.
Here's how you can use the YOLO11 `DetectionTrainer` and customize it.
```python
from ultralytics.models.yolo.detect import DetectionTrainer
@ -96,9 +96,9 @@ There are other components that can be customized similarly like `Validators` an
## FAQ
### How do I customize the Ultralytics YOLOv8 DetectionTrainer for specific tasks?
### How do I customize the Ultralytics YOLO11 DetectionTrainer for specific tasks?
To customize the Ultralytics YOLOv8 `DetectionTrainer` for a specific task, you can override its methods to adapt to your custom model and dataloader. Start by inheriting from `DetectionTrainer` and then redefine methods like `get_model` to implement your custom functionalities. Here's an example:
To customize the Ultralytics YOLO11 `DetectionTrainer` for a specific task, you can override its methods to adapt to your custom model and dataloader. Start by inheriting from `DetectionTrainer` and then redefine methods like `get_model` to implement your custom functionalities. Here's an example:
```python
from ultralytics.models.yolo.detect import DetectionTrainer
@ -117,18 +117,18 @@ trained_model = trainer.best # get best model
For further customization like changing the `loss function` or adding a `callback`, you can reference our [Callbacks Guide](../usage/callbacks.md).
### What are the key components of the BaseTrainer in Ultralytics YOLOv8?
### What are the key components of the BaseTrainer in Ultralytics YOLO11?
The `BaseTrainer` in Ultralytics YOLOv8 serves as the foundation for training routines and can be customized for various tasks by overriding its generic methods. Key components include:
The `BaseTrainer` in Ultralytics YOLO11 serves as the foundation for training routines and can be customized for various tasks by overriding its generic methods. Key components include:
- `get_model(cfg, weights)` to build the model to be trained.
- `get_dataloader()` to build the dataloader.
For more details on the customization and source code, see the [`BaseTrainer` Reference](../reference/engine/trainer.md).
### How can I add a callback to the Ultralytics YOLOv8 DetectionTrainer?
### How can I add a callback to the Ultralytics YOLO11 DetectionTrainer?
You can add callbacks to monitor and modify the training process in Ultralytics YOLOv8 `DetectionTrainer`. For instance, here's how you can add a callback to log model weights after every training [epoch](https://www.ultralytics.com/glossary/epoch):
You can add callbacks to monitor and modify the training process in Ultralytics YOLO11 `DetectionTrainer`. For instance, here's how you can add a callback to log model weights after every training [epoch](https://www.ultralytics.com/glossary/epoch):
```python
from ultralytics.models.yolo.detect import DetectionTrainer
@ -148,19 +148,19 @@ trainer.train()
For further details on callback events and entry points, refer to our [Callbacks Guide](../usage/callbacks.md).
### Why should I use Ultralytics YOLOv8 for model training?
### Why should I use Ultralytics YOLO11 for model training?
Ultralytics YOLOv8 offers a high-level abstraction on powerful engine executors, making it ideal for rapid development and customization. Key benefits include:
Ultralytics YOLO11 offers a high-level abstraction on powerful engine executors, making it ideal for rapid development and customization. Key benefits include:
- **Ease of Use**: Both command-line and Python interfaces simplify complex tasks.
- **Performance**: Optimized for real-time [object detection](https://www.ultralytics.com/glossary/object-detection) and various vision AI applications.
- **Customization**: Easily extendable for custom models, [loss functions](https://www.ultralytics.com/glossary/loss-function), and dataloaders.
Learn more about YOLOv8's capabilities by visiting [Ultralytics YOLO](https://www.ultralytics.com/yolo).
Learn more about YOLO11's capabilities by visiting [Ultralytics YOLO](https://www.ultralytics.com/yolo).
### Can I use the Ultralytics YOLOv8 DetectionTrainer for non-standard models?
### Can I use the Ultralytics YOLO11 DetectionTrainer for non-standard models?
Yes, Ultralytics YOLOv8 `DetectionTrainer` is highly flexible and can be customized for non-standard models. By inheriting from `DetectionTrainer`, you can overload different methods to support your specific model's needs. Here's a simple example:
Yes, Ultralytics YOLO11 `DetectionTrainer` is highly flexible and can be customized for non-standard models. By inheriting from `DetectionTrainer`, you can overload different methods to support your specific model's needs. Here's a simple example:
```python
from ultralytics.models.yolo.detect import DetectionTrainer