fine_tuning_steps.md

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📝 Fine-Tuning Convolutional Neural Networks (CNNs)

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✅ Step-by-Step Checklist

1. Setup

• Load a pretrained CNN backbone (e.g., ResNet50 pretrained on ImageNet).

• Replace the classification head with a new fully connected layer:

  • nn.Linear(2048, num_classes) for ResNet50.

• Freeze all layers except the classification head.

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2. Data Preparation

• Resize inputs to the resolution expected by the pretrained model (e.g., 384×384).

• Normalize images using pretrained stats (ImageNet mean & std).

• Apply augmentation:

  • Basic: random crop, horizontal flip, small rotations.

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3. Hyperparameters

• Learning rate:
• Batch size:
• Epochs:
• Optimizer:
  • AdamW (fast convergence, but watch for overfitting).
• Weight decay: 1e-4 (regularization).

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4. Learning Rate Strategy

• Warm-up: start small (1e-6) and ramp to base LR over 5–10 epochs.
• Decay:
  • Step decay (×0.1 every 30 epochs).
• Scheduler:
  • StepLR, CosineAnnealingLR, or OneCycleLR.

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5. Overfitting Prevention

• Use data augmentation to increase variability.
• Add dropout (0.2–0.5) in classifier head.
• Apply early stopping based on validation loss.

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