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76 lines
1.5 KiB
76 lines
1.5 KiB
2 years ago
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## Callbacks
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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.
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## Examples
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### Returning additional information with Prediction
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In this example, we want to return the original frame with each result object. Here's how we can do that
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```python
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def on_predict_batch_end(predictor):
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# results -> List[batch_size]
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_, _, im0s, _, _ = predictor.batch
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im0s = im0s if isinstance(im0s, list) else [im0s]
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predictor.results = zip(predictor.results, im0s)
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model = YOLO(f"yolov8n.pt")
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model.add_callback("on_predict_batch_end", on_predict_batch_end)
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for (result, frame) in model.track/predict():
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pass
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```
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## All callbacks
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Here are all supported callbacks.
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### Trainer
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`on_pretrain_routine_start`
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`on_pretrain_routine_end`
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`on_train_start`
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`on_train_epoch_start`
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`on_train_batch_start`
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`optimizer_step`
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`on_before_zero_grad`
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`on_train_batch_end`
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`on_train_epoch_end`
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`on_fit_epoch_end`
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`on_model_save`
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`on_train_end`
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`on_params_update`
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`teardown`
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### Validator
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`on_val_start`
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`on_val_batch_start`
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`on_val_batch_end`
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`on_val_end`
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### Predictor
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`on_predict_start`
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`on_predict_batch_start`
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`on_predict_postprocess_end`
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`on_predict_batch_end`
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`on_predict_end`
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### Exporter
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`on_export_start`
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`on_export_end`
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