---
title: BackboneFinetuning
description: Finetune a backbone model based on a learning rate user-defined scheduling.
canonical: >-
  https://lightning.ai/docs/pytorch/stable/optional-api/api/lightning.pytorch.callbacks.BackboneFinetuning
---
# BackboneFinetuning {#backbonefinetuning}

### `class lightning.pytorch.callbacks.BackboneFinetuning(unfreeze_backbone_at_epoch=10, lambda_func=<function multiplicative>, backbone_initial_ratio_lr=0.1, backbone_initial_lr=None, should_align=True, initial_denom_lr=10.0, train_bn=True, verbose=False, rounding=12)` {#lightning.pytorch.callbacks.BackboneFinetuning}

Bases: [`BaseFinetuning`](/docs/pytorch/2.5.5/optional-api/api/lightning.pytorch.callbacks.BaseFinetuning.md#lightning.pytorch.callbacks.BaseFinetuning "lightning.pytorch.callbacks.finetuning.BaseFinetuning")

Finetune a backbone model based on a learning rate user-defined scheduling.

When the backbone learning rate reaches the current model learning rate and `should_align` is set to True, it will align with it for the rest of the training.

Parameters:

*   **unfreeze\_backbone\_at\_epoch** ([`int`](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)")) – Epoch at which the backbone will be unfreezed.
    
*   **lambda\_func** ([`Callable`](https://docs.python.org/3/library/typing.html#typing.Callable "(in Python v3.15)")) – Scheduling function for increasing backbone learning rate.
    
*   **backbone\_initial\_ratio\_lr** ([`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)")) – Used to scale down the backbone learning rate compared to rest of model
    
*   **backbone\_initial\_lr** ([`Optional`](https://docs.python.org/3/library/typing.html#typing.Optional "(in Python v3.15)")\[[`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)")\]) – Optional, Initial learning rate for the backbone. By default, we will use `current_learning /  backbone_initial_ratio_lr`
    
*   **should\_align** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool "(in Python v3.15)")) – Whether to align with current learning rate when backbone learning reaches it.
    
*   **initial\_denom\_lr** ([`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)")) – When unfreezing the backbone, the initial learning rate will `current_learning_rate /  initial_denom_lr`.
    
*   **train\_bn** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool "(in Python v3.15)")) – Whether to make Batch Normalization trainable.
    
*   **verbose** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool "(in Python v3.15)")) – Display current learning rate for model and backbone
    
*   **rounding** ([`int`](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)")) – Precision for displaying learning rate
    

Example:

```python
>>> from lightning.pytorch import Trainer
>>> from lightning.pytorch.callbacks import BackboneFinetuning
>>> multiplicative = lambda epoch: 1.5
>>> backbone_finetuning = BackboneFinetuning(200, multiplicative)
>>> trainer = Trainer(callbacks=[backbone_finetuning])
```

#### `finetune_function(pl_module, epoch, optimizer)` {#lightning.pytorch.callbacks.BackboneFinetuning.finetune_function}

Called when the epoch begins.

Return type:

[`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)")

#### `freeze_before_training(pl_module)` {#lightning.pytorch.callbacks.BackboneFinetuning.freeze_before_training}

Override to add your freeze logic.

Return type:

[`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)")

#### `load_state_dict(state_dict)` {#lightning.pytorch.callbacks.BackboneFinetuning.load_state_dict}

Called when loading a checkpoint, implement to reload callback state given callback’s `state_dict`.

Parameters:

**state\_dict** ([`dict`](https://docs.python.org/3/builtins/stdtypes.html#dict "(in Python v3.15)")\[[`str`](https://docs.python.org/3/builtins/stdtypes.html#str "(in Python v3.15)"), [`Any`](https://docs.python.org/3/library/typing.html#typing.Any "(in Python v3.15)")\]) – the callback state returned by `state_dict`.

Return type:

[`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)")

#### `on_fit_start(trainer, pl_module)` {#lightning.pytorch.callbacks.BackboneFinetuning.on_fit_start}

Raises:

**MisconfigurationException** – If LightningModule has no nn.Module backbone attribute.

Return type:

[`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)")

#### `state_dict()` {#lightning.pytorch.callbacks.BackboneFinetuning.state_dict}

Called when saving a checkpoint, implement to generate callback’s `state_dict`.

Return type:

[`dict`](https://docs.python.org/3/builtins/stdtypes.html#dict "(in Python v3.15)")\[[`str`](https://docs.python.org/3/builtins/stdtypes.html#str "(in Python v3.15)"), [`Any`](https://docs.python.org/3/library/typing.html#typing.Any "(in Python v3.15)")\]

Returns:

A dictionary containing callback state.

---

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