description: Explore Ultralytics callbacks for training, validation, exporting, and prediction. Learn how to use and customize them for your ML models.
Ultralytics framework supports callbacks as entry points in strategic stages of train, val, export, and predict modes. Each callback accepts a `Trainer`, `Validator`, or `Predictor` object depending on the operation type. All properties of these objects can be found in Reference section of the docs.
Here are all supported callbacks. See callbacks [source code](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/base.py) for additional details.
### What are Ultralytics callbacks and how can I use them?
**Ultralytics callbacks** are specialized entry points triggered during key stages of model operations like training, validation, exporting, and prediction. These callbacks allow for custom functionality at specific points in the process, enabling enhancements and modifications to the workflow. Each callback accepts a `Trainer`, `Validator`, or `Predictor` object, depending on the operation type. For detailed properties of these objects, refer to the [Reference section](../reference/cfg/__init__.md).
To use a callback, you can define a function and then add it to the model with the `add_callback` method. Here's an example of how to return additional information during prediction:
```python
from ultralytics import YOLO
def on_predict_batch_end(predictor):
"""Handle prediction batch end by combining results with corresponding frames; modifies predictor results."""
_, image, _, _ = predictor.batch
image = image if isinstance(image, list) else [image]
### How can I customize Ultralytics training routine using callbacks?
To customize your Ultralytics training routine using callbacks, you can inject your logic at specific stages of the training process. Ultralytics YOLO provides a variety of training callbacks such as `on_train_start`, `on_train_end`, and `on_train_batch_end`. These allow you to add custom metrics, processing, or logging.
Here's an example of how to log additional metrics at the end of each training epoch:
```python
from ultralytics import YOLO
def on_train_epoch_end(trainer):
"""Custom logic for additional metrics logging at the end of each training epoch."""
Refer to the [Training Guide](../modes/train.md) for more details on how to effectively use training callbacks.
### Why should I use callbacks during validation in Ultralytics YOLO?
Using **callbacks during validation** in Ultralytics YOLO can enhance model evaluation by allowing custom processing, logging, or metrics calculation. Callbacks such as `on_val_start`, `on_val_batch_end`, and `on_val_end` provide entry points to inject custom logic, ensuring detailed and comprehensive validation processes.
For instance, you might want to log additional validation metrics or save intermediate results for further analysis. Here's an example of how to log custom metrics at the end of validation:
Check out the [Validation Guide](../modes/val.md) for further insights on incorporating callbacks into your validation process.
### How do I attach a custom callback for the prediction mode in Ultralytics YOLO?
To attach a custom callback for the **prediction mode** in Ultralytics YOLO, you define a callback function and register it with the prediction process. Common prediction callbacks include `on_predict_start`, `on_predict_batch_end`, and `on_predict_end`. These allow for modification of prediction outputs and integration of additional functionalities like data logging or result transformation.
Here is an example where a custom callback is used to log predictions:
For more comprehensive usage, refer to the [Prediction Guide](../modes/predict.md) which includes detailed instructions and additional customization options.
### What are some practical examples of using callbacks in Ultralytics YOLO?
Ultralytics YOLO supports various practical implementations of callbacks to enhance and customize different phases like training, validation, and prediction. Some practical examples include:
1.**Logging Custom Metrics**: Log additional metrics at different stages, such as the end of training or validation epochs.
2.**[Data Augmentation](https://www.ultralytics.com/glossary/data-augmentation)**: Implement custom data transformations or augmentations during prediction or training batches.
Explore the [Complete Callback Reference](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/base.py) to find more options and examples.
