---
comments: true
description: Explore Ultralytics callbacks for training, validation, exporting, and prediction. Learn how to use and customize them for your ML models.
keywords: Ultralytics, callbacks, training, validation, export, prediction, ML models, YOLO, Python, machine learning
---

# Callbacks

Ultralytics framework supports callbacks, which serve as entry points at strategic stages during the `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 are detailed in the [Reference section](../reference/cfg/__init__.md) of the documentation.

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  <strong>Watch:</strong> How to use Ultralytics Callbacks | Predict, Train, Validate and Export Callbacks | Ultralytics YOLO🚀
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## Examples

### Returning Additional Information with Prediction

In this example, we demonstrate how to return the original frame along with each result object:

```python
from ultralytics import YOLO


def on_predict_batch_end(predictor):
    """Combine prediction results with corresponding frames."""
    _, image, _, _ = predictor.batch

    # Ensure that image is a list
    image = image if isinstance(image, list) else [image]

    # Combine the prediction results with the corresponding frames
    predictor.results = zip(predictor.results, image)


# Create a YOLO model instance
model = YOLO("yolo26n.pt")

# Add the custom callback to the model
model.add_callback("on_predict_batch_end", on_predict_batch_end)

# Iterate through the results and frames
for result, frame in model.predict():  # or model.track()
    pass
```

### Access Model metrics using the `on_model_save` callback

This example shows how to retrieve training details, such as the best_fitness score, total_loss, and other metrics after a checkpoint is saved using the `on_model_save` callback.

```python
from ultralytics import YOLO

# Load a YOLO model
model = YOLO("yolo26n.pt")


def print_checkpoint_metrics(trainer):
    """Print trainer metrics and loss details after each checkpoint is saved."""
    print(
        f"Model details\n"
        f"Best fitness: {trainer.best_fitness}, "
        f"Loss names: {trainer.loss_names}, "  # List of loss names
        f"Metrics: {trainer.metrics}, "
        f"Total loss: {trainer.tloss}"  # Total loss value
    )


if __name__ == "__main__":
    # Add on_model_save callback.
    model.add_callback("on_model_save", print_checkpoint_metrics)

    # Run model training on custom dataset.
    results = model.train(data="coco8.yaml", epochs=3)
```

## All Callbacks

Below are all the supported callbacks. For more details, refer to the callbacks [source code](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/base.py).

### Trainer Callbacks

| Callback                    | Description                                                                                                                                                                                                                                                                                                                      |
| --------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `on_pretrain_routine_start` | Triggered at the beginning of the pre-training routine, before data loading and model setup.                                                                                                                                                                                                                                     |
| `on_pretrain_routine_end`   | Triggered at the end of the pre-training routine, after data loading and model setup are complete.                                                                                                                                                                                                                               |
| `on_train_start`            | Triggered when the training starts, before the first [epoch](https://www.ultralytics.com/glossary/epoch) begins.                                                                                                                                                                                                                 |
| `on_train_epoch_start`      | Triggered at the start of each training [epoch](https://www.ultralytics.com/glossary/epoch), before batch iteration begins.                                                                                                                                                                                                      |
| `on_train_batch_start`      | Triggered at the start of each training batch, before the forward pass.                                                                                                                                                                                                                                                          |
| `optimizer_step`            | Triggered during the optimizer step. Reserved for custom integrations; not called by the default training loop.                                                                                                                                                                                                                  |
| `on_before_zero_grad`       | Triggered before gradients are zeroed. Reserved for custom integrations; not called by the default training loop.                                                                                                                                                                                                                |
| `on_train_batch_end`        | Triggered at the end of each training batch, after the backward pass. The optimizer step may be deferred due to gradient accumulation.                                                                                                                                                                                           |
| `on_train_epoch_end`        | Triggered at the end of each training epoch, after all batches are processed but **before** validation. Validation metrics and fitness may not be available yet.                                                                                                                                                                 |
| `on_model_save`             | Triggered when the model checkpoint is saved, after validation.                                                                                                                                                                                                                                                                  |
| `on_fit_epoch_end`          | Triggered at the end of each fit epoch (train + val), **after** validation and any checkpoint save. Validation metrics are available, and fitness is available for the per-epoch training call. This callback is also called during final best-model evaluation, where no checkpoint save occurs and fitness may not be present. |
| `on_train_end`              | Triggered when the training process ends, after final evaluation of the best model.                                                                                                                                                                                                                                              |
| `on_params_update`          | Triggered when model parameters are updated. Reserved for custom integrations; not called by the default training loop.                                                                                                                                                                                                          |
| `teardown`                  | Triggered when the training process is being cleaned up.                                                                                                                                                                                                                                                                         |

### Validator Callbacks

| Callback             | Description                                      |
| -------------------- | ------------------------------------------------ |
| `on_val_start`       | Triggered when validation starts.                |
| `on_val_batch_start` | Triggered at the start of each validation batch. |
| `on_val_batch_end`   | Triggered at the end of each validation batch.   |
| `on_val_end`         | Triggered when validation ends.                  |

### Predictor Callbacks

| Callback                     | Description                                         |
| ---------------------------- | --------------------------------------------------- |
| `on_predict_start`           | Triggered when the prediction process starts.       |
| `on_predict_batch_start`     | Triggered at the start of each prediction batch.    |
| `on_predict_postprocess_end` | Triggered at the end of prediction post-processing. |
| `on_predict_batch_end`       | Triggered at the end of each prediction batch.      |
| `on_predict_end`             | Triggered when the prediction process ends.         |

### Exporter Callbacks

| Callback          | Description                               |
| ----------------- | ----------------------------------------- |
| `on_export_start` | Triggered when the export process starts. |
| `on_export_end`   | Triggered when the export process ends.   |

## FAQ

### What are Ultralytics callbacks and how can I use them?

Ultralytics callbacks are specialized entry points that are triggered during key stages of model operations such as training, validation, exporting, and prediction. These callbacks enable custom functionality at specific points in the process, allowing for 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, define a function and add it to the model using the [`model.add_callback()`](../reference/engine/model.md#ultralytics.engine.model.Model.add_callback) method. Here is an example of returning 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]
    predictor.results = zip(predictor.results, image)


model = YOLO("yolo26n.pt")
model.add_callback("on_predict_batch_end", on_predict_batch_end)
for result, frame in model.predict():
    pass
```

### How can I customize the Ultralytics training routine using callbacks?

Customize your Ultralytics training routine by injecting 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`, which allow you to add custom metrics, processing, or logging.

Here's how to freeze BatchNorm statistics when freezing layers with callbacks:

```python
from ultralytics import YOLO


# Add a callback to put the frozen layers in eval mode to prevent BN values from changing
def put_in_eval_mode(trainer):
    n_layers = trainer.args.freeze
    if not isinstance(n_layers, int):
        return

    for i, (name, module) in enumerate(trainer.model.named_modules()):
        if name.endswith("bn") and int(name.split(".")[1]) < n_layers:
            module.eval()
            module.track_running_stats = False


model = YOLO("yolo26n.pt")
model.add_callback("on_train_epoch_start", put_in_eval_mode)
model.train(data="coco.yaml", epochs=10)
```

For more details on effectively using training callbacks, see the [Training Guide](../modes/train.md).

### Why should I use callbacks during validation in Ultralytics YOLO?

Using callbacks during validation in Ultralytics YOLO enhances model evaluation by enabling custom processing, logging, or metrics calculation. Callbacks like `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 example, to plot all validation batches instead of just the first three:

```python
import inspect

from ultralytics import YOLO


def plot_samples(validator):
    frame = inspect.currentframe().f_back.f_back
    v = frame.f_locals
    validator.plot_val_samples(v["batch"], v["batch_i"])
    validator.plot_predictions(v["batch"], v["preds"], v["batch_i"])


model = YOLO("yolo26n.pt")
model.add_callback("on_val_batch_end", plot_samples)
model.val(data="coco.yaml")
```

For more insights on incorporating callbacks into your validation process, see the [Validation Guide](../modes/val.md).

### How do I attach a custom callback for the prediction mode in Ultralytics YOLO?

To attach a custom callback for prediction mode in Ultralytics YOLO, 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 the modification of prediction outputs and the integration of additional functionalities, like data logging or result transformation.

Here is an example where a custom callback saves predictions based on whether an object of a particular class is present:

```python
from ultralytics import YOLO

model = YOLO("yolo26n.pt")

class_id = 2


def save_on_object(predictor):
    r = predictor.results[0]
    if class_id in r.boxes.cls:
        predictor.args.save = True
    else:
        predictor.args.save = False


model.add_callback("on_predict_postprocess_end", save_on_object)
results = model("pedestrians.mp4", stream=True, save=True)

for results in results:
    pass
```

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:

- **Logging Custom Metrics**: Log additional metrics at different stages, such as at the end of training or validation [epochs](https://www.ultralytics.com/glossary/epoch).
- **[Data Augmentation](https://www.ultralytics.com/glossary/data-augmentation)**: Implement custom data transformations or augmentations during prediction or training batches.
- **Intermediate Results**: Save intermediate results, such as predictions or frames, for further analysis or visualization.

Example: Combining frames with prediction results during prediction using `on_predict_batch_end`:

```python
from ultralytics import YOLO


def on_predict_batch_end(predictor):
    """Combine prediction results with frames."""
    _, image, _, _ = predictor.batch
    image = image if isinstance(image, list) else [image]
    predictor.results = zip(predictor.results, image)


model = YOLO("yolo26n.pt")
model.add_callback("on_predict_batch_end", on_predict_batch_end)
for result, frame in model.predict():
    pass
```

Explore the [callback source code](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/base.py) for more options and examples.
