# Custom training fitness
A fitness callback selects best checkpoints and controls patience with a custom scalar score.
Verified against LibreYOLO v1.6.0.

## Define the scorer

**Python**

```python
from libreyolo import LibreYOLO

class RecallFitness:
    def fitness(self, metrics):
        return float(metrics["metrics/recall(B)"])

model = LibreYOLO("LibreYOLO9s.pt", device="cpu")
model.train(data="coco8.yaml", epochs=1, workers=0, device="cpu", callbacks=RecallFitness())
```

Implement `fitness(metrics)` on an object passed through `callbacks=`. The input is the read-only scalar mapping used by `TrainEpochEvent.val_metrics`. Return one finite real number; higher is better. A callback list may contain only one scorer. VLM and VLA fine-tuning do not accept a fitness callback.

## Checkpoint selection

Scoring runs on rank zero for each validation result used for selection, before checkpoint writing and epoch callbacks. It changes best-checkpoint selection and patience without replacing the original metrics.

Checkpoints record `fitness_source="callback"`. Callback code and state are not serialized, so custom-fitness training cannot resume. Start a new run from the saved weights instead.

See [validation](/docs/train/validation) for the metric keys available to a scorer.