Add FAQ sections to Modes and Tasks (#14181)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Abirami Vina <abirami.vina@gmail.com>
Co-authored-by: RizwanMunawar <chr043416@gmail.com>
Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>

docs/en/usage/engine.md

@@ -93,3 +93,87 @@ To know more about Callback triggering events and entry point, checkout our [Cal
 ## Other engine components
 
 There are other components that can be customized similarly like `Validators` and `Predictors`. See Reference section for more information on these.
+
+## FAQ
+
+### How do I customize the Ultralytics YOLOv8 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:
+
+```python
+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={...})
+trainer.train()
+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?
+
+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:
+
+- `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?
+
+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:
+
+```python
+from ultralytics.models.yolo.detect import DetectionTrainer
+
+
+# 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
+    print(last_weight_path)
+
+
+trainer = DetectionTrainer(overrides={...})
+trainer.add_callback("on_train_epoch_end", log_model)  # Adds to existing callbacks
+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?
+
+Ultralytics YOLOv8 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 and various vision AI applications.
+- **Customization**: Easily extendable for custom models, loss functions, and dataloaders.
+
+Learn more about YOLOv8's capabilities by visiting [Ultralytics YOLO](https://www.ultralytics.com/yolo).
+
+### Can I use the Ultralytics YOLOv8 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:
+
+```python
+from ultralytics.models.yolo.detect import DetectionTrainer
+
+
+class CustomDetectionTrainer(DetectionTrainer):
+    def get_model(self, cfg, weights):
+        """Loads a custom detection model."""
+        ...
+
+
+trainer = CustomDetectionTrainer(overrides={...})
+trainer.train()
+```
+
+For more comprehensive instructions and examples, review the [DetectionTrainer](../reference/engine/trainer.md) documentation.