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# 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 of the documentation.

Watch: How to use Ultralytics Callbacks | Predict, Train, Validate and Export Callbacks | Ultralytics YOLO🚀

## Examples

### Returning Additional Information with Prediction

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

    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("yolo11n.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.

    from ultralytics import YOLO

    # Load a YOLO model
    model = YOLO("yolo11n.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.

### Trainer Callbacks

Callback | Description
--- | ---
`on_pretrain_routine_start` | Triggered at the beginning of the pre-training routine.
`on_pretrain_routine_end` | Triggered at the end of the pre-training routine.
`on_train_start` | Triggered when the training starts.
`on_train_epoch_start` | Triggered at the start of each training epoch.
`on_train_batch_start` | Triggered at the start of each training batch.
`optimizer_step` | Triggered during the optimizer step.
`on_before_zero_grad` | Triggered before gradients are zeroed.
`on_train_batch_end` | Triggered at the end of each training batch.
`on_train_epoch_end` | Triggered at the end of each training epoch.
`on_fit_epoch_end` | Triggered at the end of each fit epoch.
`on_model_save` | Triggered when the model is saved.
`on_train_end` | Triggered when the training process ends.
`on_params_update` | Triggered when model parameters are updated.
`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.

To use a callback, define a function and add it to the model using the `model.add_callback()` method. Here is an example of returning additional information during prediction:

    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("yolo11n.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:

    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("yolo11n.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.

### 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:

    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("yolo11n.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.

### 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:

    from ultralytics import YOLO

    model = YOLO("yolo11n.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, 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.
- 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`:

    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("yolo11n.pt")
    model.add_callback("on_predict_batch_end", on_predict_batch_end)
    for result, frame in model.predict():
        pass

Explore the callback source code for more options and examples.

📅 Created 1 year ago ✏️ Updated 4 months ago

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