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: Mastering Ultralytics YOLO: Callbacks

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

All Callbacks

Below are all the supported callbacks. For more details, refer to the callbacks source code.

Trainer Callbacks

Validator Callbacks

Predictor Callbacks

Exporter Callbacks

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 8 days ago

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