Cv Frozen Batchnorm Finetuning

Frozen BatchNorm Fine-Tuning

Overview

When fine-tuning a pretrained backbone on a small medical/domain-specific dataset, unfreezing all layers including BatchNorm can destabilize training. BN layers maintain running mean/variance from ImageNet — updating these with a small batch from a different domain collapses the statistics. The fix: unfreeze convolutional/linear layers for gradient updates but explicitly keep all BatchNorm layers frozen (eval mode). This preserves pretrained normalization while allowing the rest of the network to adapt.

Quick Start

pythonCopy code
import torch.nn as nn

def unfreeze_with_frozen_bn(model, n_layers=20):
    """Unfreeze last N backbone layers, keep all BatchNorm frozen."""
    # First freeze everything
    for param in model.parameters():
        param.requires_grad = False

    # Unfreeze last N layers, skipping BN
    layers = list(model.features.children())
    for layer in layers[-n_layers:]:
        for name, module in layer.named_modules():
            if isinstance(module, (nn.BatchNorm2d, nn.BatchNorm1d)):
                module.eval()  # keep in eval mode
                for param in module.parameters():
                    param.requires_grad = False
            else:
                for param in module.parameters():
                    param.requires_grad = True

    # Always unfreeze the classifier head
    for param in model.classifier.parameters():
        param.requires_grad = True

# Also override train() to keep BN in eval mode
class FrozenBNModel(nn.Module):
    def train(self, mode=True):
        super().train(mode)
        for m in self.modules():
            if isinstance(m, (nn.BatchNorm2d, nn.BatchNorm1d)):
                m.eval()
        return self

Workflow

1. Load a pretrained model (EfficientNet, DenseNet, ResNet)
2. Freeze all parameters
3. Unfreeze the last N backbone layers — but skip BatchNorm modules
4. Always unfreeze the classifier head
5. Override train() to force BN layers into eval mode every forward pass

Key Decisions

• N layers: Start with last 20; increase if underfitting, decrease if unstable
• Override train(): Critical — PyTorch's model.train() re-enables BN by default
• GroupNorm/LayerNorm: These don't use running statistics — safe to unfreeze
• Small datasets: More important when training data < 5000 images

References

• RSNA EfficientNet Starter Notebook