File size: 4,443 Bytes
8166792 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
---
comments: true
description: Learn how to leverage callbacks in Ultralytics YOLO framework to perform custom tasks in trainer, validator, predictor and exporter modes.
keywords: callbacks, Ultralytics framework, Trainer, Validator, Predictor, Exporter, train, val, export, predict, YOLO, Object Detection
---
## Callbacks
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.
## Examples
### Returning additional information with Prediction
In this example, we want to return the original frame with each result object. Here's how we can do that
```python
def on_predict_batch_end(predictor):
# Retrieve the batch data
_, im0s, _, _ = predictor.batch
# Ensure that im0s is a list
im0s = im0s if isinstance(im0s, list) else [im0s]
# Combine the prediction results with the corresponding frames
predictor.results = zip(predictor.results, im0s)
# Create a YOLO model instance
model = YOLO(f'yolov8n.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.track/predict():
pass
```
## All callbacks
Here are all supported callbacks. See callbacks [source code](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/yolo/utils/callbacks/base.py) for additional details.
### Trainer Callbacks
| Callback | Description |
|-----------------------------|---------------------------------------------------------|
| `on_pretrain_routine_start` | Triggered at the beginning of pre-training routine |
| `on_pretrain_routine_end` | Triggered at the end of 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 the 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 the 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 postprocessing |
| `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 | |