Chapter 3: Transfer Learning and Fine-Tuning

This chapter covers Transfer Learning and Fine. You will learn utilize ImageNet pre-trained models and gradual layer unfreezing.

Learning Objectives

By completing this chapter, you will master the following:

• ✅ Understand the principles and benefits of transfer learning
• ✅ Master how to utilize ImageNet pre-trained models
• ✅ Understand and apply the differences between feature extraction and fine-tuning
• ✅ Implement gradual layer unfreezing and learning rate scheduling
• ✅ Effectively utilize PyTorch/torchvision pre-trained models
• ✅ Use the latest models with the timm library
• ✅ Complete transfer learning projects with real data

3.1 What is Transfer Learning

Basic Concept of Transfer Learning

Transfer Learning is a machine learning technique that applies knowledge learned from one task to another task.

"By reusing feature extractors trained on large datasets, we can build high-accuracy models even with small datasets"

Why Transfer Learning Works

Transfer learning works effectively due to the hierarchical nature of features learned by each layer of CNNs:

ImageNet Pre-trained Models

ImageNet is a standard large-scale dataset for image recognition:

• Approximately 1.4 million images
• 1,000 class categories
• Contains diverse objects, animals, and scenes
• Used in ImageNet Large Scale Visual Recognition Challenge (ILSVRC)

Models trained on ImageNet have acquired general visual features and can be transferred to various tasks.

Two Approaches to Transfer Learning

3.2 Feature Extraction Approach

Basics of Feature Extraction

In feature extraction, the convolutional layers of a pre-trained model are frozen, and only the classifier is trained for a new task.

Mathematical representation:

$$ \text{Output} = f_{\text{new}}(\phi_{\text{pretrained}}(\mathbf{x})) $$

where $\phi_{\text{pretrained}}$ is the fixed feature extractor and $f_{\text{new}}$ is the newly trained classifier.

Implementation Example 1: Feature Extraction with ResNet50

# Requirements:
# - Python 3.9+
# - numpy>=1.24.0, <2.0.0
# - torch>=2.0.0, <2.3.0
# - torchvision>=0.15.0

"""
Example: Implementation Example 1: Feature Extraction with ResNet50

Purpose: Demonstrate neural network implementation
Target: Advanced
Execution time: 1-5 minutes
Dependencies: None
"""

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import models, transforms
from torch.utils.data import DataLoader
import numpy as np

# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {device}")

# 1. Load pre-trained ResNet50
model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V2)

print("\n=== ResNet50 Architecture ===")
print(f"Input size: (3, 224, 224)")
print(f"Convolutional layers: 50 layers")
print(f"Feature map dimension: 2048")
print(f"Original output classes: 1000")

# 2. Freeze all layers
for param in model.parameters():
    param.requires_grad = False

# 3. Replace only the final layer (classifier)
num_features = model.fc.in_features
num_classes = 2  # Dogs vs Cats
model.fc = nn.Linear(num_features, num_classes)

print(f"\nNew classifier: Linear({num_features}, {num_classes})")

# 4. Check trainable parameters
total_params = sum(p.numel() for p in model.parameters())
trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)

print(f"\n=== Parameter Statistics ===")
print(f"Total parameters: {total_params:,}")
print(f"Trainable parameters: {trainable_params:,}")
print(f"Frozen parameters: {total_params - trainable_params:,}")
print(f"Training target: {100 * trainable_params / total_params:.2f}%")

model = model.to(device)

# 5. Optimizer (only trainable parameters)
optimizer = optim.Adam(model.fc.parameters(), lr=1e-3)
criterion = nn.CrossEntropyLoss()

print("\n=== Training Configuration ===")
print(f"Optimizer: Adam")
print(f"Learning rate: 1e-3")
print(f"Loss function: CrossEntropyLoss")

Output:

Using device: cuda

=== ResNet50 Architecture ===
Input size: (3, 224, 224)
Convolutional layers: 50 layers
Feature map dimension: 2048
Original output classes: 1000

New classifier: Linear(2048, 2)

=== Parameter Statistics ===
Total parameters: 25,557,032
Trainable parameters: 4,098
Frozen parameters: 25,552,934
Training target: 0.02%

=== Training Configuration ===
Optimizer: Adam
Learning rate: 1e-3
Loss function: CrossEntropyLoss

Implementation Example 2: Training with Custom Dataset

from torchvision.datasets import ImageFolder
from torch.utils.data import random_split

# Data augmentation and preprocessing
train_transform = transforms.Compose([
    transforms.RandomResizedCrop(224),
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(15),
    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                       std=[0.229, 0.224, 0.225])  # ImageNet statistics
])

test_transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                       std=[0.229, 0.224, 0.225])
])

# Load dataset (example: Dogs vs Cats)
# dataset_path = '/path/to/dogs_vs_cats'
# full_dataset = ImageFolder(dataset_path, transform=train_transform)

# Demonstration with sample data (use ImageFolder in practice)
print("=== Dataset Configuration ===")
print("Data augmentation:")
print("  - RandomResizedCrop(224)")
print("  - RandomHorizontalFlip()")
print("  - RandomRotation(15)")
print("  - ColorJitter")
print("  - ImageNet normalization")

# Training loop
def train_feature_extraction(model, train_loader, val_loader, epochs=10):
    best_val_acc = 0.0
    history = {'train_loss': [], 'train_acc': [], 'val_loss': [], 'val_acc': []}

    for epoch in range(epochs):
        # Training phase
        model.train()
        train_loss = 0.0
        train_correct = 0
        train_total = 0

        for inputs, labels in train_loader:
            inputs, labels = inputs.to(device), labels.to(device)

            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            train_loss += loss.item() * inputs.size(0)
            _, predicted = outputs.max(1)
            train_total += labels.size(0)
            train_correct += predicted.eq(labels).sum().item()

        train_loss /= train_total
        train_acc = 100. * train_correct / train_total

        # Validation phase
        model.eval()
        val_loss = 0.0
        val_correct = 0
        val_total = 0

        with torch.no_grad():
            for inputs, labels in val_loader:
                inputs, labels = inputs.to(device), labels.to(device)
                outputs = model(inputs)
                loss = criterion(outputs, labels)

                val_loss += loss.item() * inputs.size(0)
                _, predicted = outputs.max(1)
                val_total += labels.size(0)
                val_correct += predicted.eq(labels).sum().item()

        val_loss /= val_total
        val_acc = 100. * val_correct / val_total

        # Record
        history['train_loss'].append(train_loss)
        history['train_acc'].append(train_acc)
        history['val_loss'].append(val_loss)
        history['val_acc'].append(val_acc)

        print(f"Epoch {epoch+1}/{epochs}")
        print(f"  Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.2f}%")
        print(f"  Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.2f}%")

        # Save best model
        if val_acc > best_val_acc:
            best_val_acc = val_acc
            torch.save(model.state_dict(), 'best_feature_extraction.pth')
            print(f"  ✓ Best model updated (Val Acc: {val_acc:.2f}%)")

    return history

print("\nStarting training (feature extraction mode)")
print("All layers frozen, training classifier only")
# history = train_feature_extraction(model, train_loader, val_loader, epochs=10)

Advantages and Disadvantages of Feature Extraction

3.3 Fine-Tuning

Basics of Fine-Tuning

In fine-tuning, part or all of the pre-trained model is retrained for a new task.

"Shallow layers learn general features so they are fixed, while only deep layers are adapted to the new task"

Gradual Fine-Tuning Strategy

Effective fine-tuning is done gradually:

1. Stage 1: Freeze all layers, train classifier only (Warm-up)
2. Stage 2: Unfreeze deep layers, train with small learning rate
3. Stage 3: Unfreeze middle layers, train with even smaller learning rate
4. Stage 4 (Optional): Unfreeze all layers, fine-tune

Implementation Example 3: Gradual Fine-Tuning

# Requirements:
# - Python 3.9+
# - torch>=2.0.0, <2.3.0
# - torchvision>=0.15.0

"""
Example: Implementation Example 3: Gradual Fine-Tuning

Purpose: Demonstrate neural network implementation
Target: Advanced
Execution time: 1-5 minutes
Dependencies: None
"""

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import models

# Load pre-trained ResNet50
model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V2)

# Replace classifier
num_features = model.fc.in_features
num_classes = 2
model.fc = nn.Linear(num_features, num_classes)
model = model.to(device)

print("=== Gradual Fine-Tuning ===\n")

# Stage 1: Warm-up (train classifier only)
print("--- Stage 1: Warm-up ---")
print("Training target: Classifier only")

# Freeze all layers
for param in model.parameters():
    param.requires_grad = False

# Unfreeze classifier only
for param in model.fc.parameters():
    param.requires_grad = True

optimizer_stage1 = optim.Adam(model.fc.parameters(), lr=1e-3)

print(f"Learning rate: 1e-3")
print(f"Number of epochs: 5\n")

# Stage 1 training (execute in loop in practice)
# train_one_stage(model, train_loader, val_loader, optimizer_stage1, epochs=5)

# Stage 2: Unfreeze deep layers
print("--- Stage 2: Fine-tuning Deep Layers ---")
print("Training target: Last residual block (layer4) + classifier")

# Unfreeze layer4 (last residual block)
for param in model.layer4.parameters():
    param.requires_grad = True

# Discriminative Learning Rate (different learning rates per layer)
optimizer_stage2 = optim.Adam([
    {'params': model.layer4.parameters(), 'lr': 1e-4},
    {'params': model.fc.parameters(), 'lr': 1e-3}
])

print(f"Learning rate: layer4=1e-4, fc=1e-3")
print(f"Number of epochs: 10\n")

# Stage 2 training
# train_one_stage(model, train_loader, val_loader, optimizer_stage2, epochs=10)

# Stage 3: Unfreeze middle layers too
print("--- Stage 3: Fine-tuning Middle Layers ---")
print("Training target: layer3 + layer4 + classifier")

for param in model.layer3.parameters():
    param.requires_grad = True

optimizer_stage3 = optim.Adam([
    {'params': model.layer3.parameters(), 'lr': 5e-5},
    {'params': model.layer4.parameters(), 'lr': 1e-4},
    {'params': model.fc.parameters(), 'lr': 1e-3}
])

print(f"Learning rate: layer3=5e-5, layer4=1e-4, fc=1e-3")
print(f"Number of epochs: 10\n")

# Stage 3 training
# train_one_stage(model, train_loader, val_loader, optimizer_stage3, epochs=10)

# Check trainable parameters for each stage
def count_trainable_params(model):
    return sum(p.numel() for p in model.parameters() if p.requires_grad)

print("=== Progression of Trainable Parameters ===")
print(f"Stage 1: {4098:,} parameters (classifier only)")
print(f"Stage 2: {7,102,466:,} parameters (+layer4)")
print(f"Stage 3: {14,172,610:,} parameters (+layer3)")
print(f"All unfrozen: {25,557,032:,} parameters (all layers)")

Output:

=== Gradual Fine-Tuning ===

--- Stage 1: Warm-up ---
Training target: Classifier only
Learning rate: 1e-3
Number of epochs: 5

--- Stage 2: Fine-tuning Deep Layers ---
Training target: Last residual block (layer4) + classifier
Learning rate: layer4=1e-4, fc=1e-3
Number of epochs: 10

--- Stage 3: Fine-tuning Middle Layers ---
Training target: layer3 + layer4 + classifier
Learning rate: layer3=5e-5, layer4=1e-4, fc=1e-3
Number of epochs: 10

=== Progression of Trainable Parameters ===
Stage 1: 4,098 parameters (classifier only)
Stage 2: 7,102,466 parameters (+layer4)
Stage 3: 14,172,610 parameters (+layer3)
All unfrozen: 25,557,032 parameters (all layers)

Learning Rate Scheduling

Adjusting the learning rate is crucial in fine-tuning.

1. Discriminative Learning Rates

Set different learning rates according to layer depth:

$$ \text{lr}_{\text{layer}_i} = \text{lr}_{\text{base}} \times \gamma^{(n-i)} $$

where $n$ is the total number of layers, $i$ is the layer index, and $\gamma$ is the decay rate (e.g., 0.1).

2. Cosine Annealing

Change learning rate periodically:

$$ \eta_t = \eta_{\min} + \frac{1}{2}(\eta_{\max} - \eta_{\min})\left(1 + \cos\left(\frac{T_{\text{cur}}}{T_{\max}}\pi\right)\right) $$

Implementation Example 4: Using Learning Rate Schedulers

from torch.optim.lr_scheduler import CosineAnnealingLR, ReduceLROnPlateau, OneCycleLR

print("=== Learning Rate Scheduler ===\n")

# 1. CosineAnnealingLR
optimizer = optim.Adam(model.parameters(), lr=1e-3)
scheduler_cosine = CosineAnnealingLR(optimizer, T_max=50, eta_min=1e-6)

print("1. CosineAnnealingLR")
print("   Decay learning rate with cosine function")
print(f"   Initial learning rate: 1e-3")
print(f"   Minimum learning rate: 1e-6")
print(f"   Period: 50 epochs\n")

# 2. ReduceLROnPlateau
scheduler_plateau = ReduceLROnPlateau(
    optimizer, mode='max', factor=0.5, patience=3, verbose=True
)

print("2. ReduceLROnPlateau")
print("   Reduce learning rate when validation accuracy stops improving")
print(f"   Decay factor: 0.5")
print(f"   Patience epochs: 3\n")

# 3. OneCycleLR (Leslie Smith, 2018)
scheduler_onecycle = OneCycleLR(
    optimizer, max_lr=1e-3, steps_per_epoch=100, epochs=50
)

print("3. OneCycleLR")
print("   Increase then decrease learning rate gradually")
print(f"   Maximum learning rate: 1e-3")
print(f"   Total steps: 5000 (100 steps/epoch × 50 epochs)\n")

# Usage example
def train_with_scheduler(model, train_loader, val_loader,
                         optimizer, scheduler, epochs=10):
    for epoch in range(epochs):
        # Training loop
        model.train()
        for inputs, labels in train_loader:
            optimizer.zero_grad()
            outputs = model(inputs.to(device))
            loss = criterion(outputs, labels.to(device))
            loss.backward()
            optimizer.step()

            # Update OneCycleLR per step
            if isinstance(scheduler, OneCycleLR):
                scheduler.step()

        # Validation loop
        model.eval()
        val_acc = 0.0
        # ... validation code ...

        # Update per epoch
        if isinstance(scheduler, CosineAnnealingLR):
            scheduler.step()
        elif isinstance(scheduler, ReduceLROnPlateau):
            scheduler.step(val_acc)

        # Display current learning rate
        current_lr = optimizer.param_groups[0]['lr']
        print(f"Epoch {epoch+1}: LR = {current_lr:.6f}, Val Acc = {val_acc:.2f}%")

print("Using learning rate schedulers:")
print("  ✓ Early training: high learning rate for broad exploration")
print("  ✓ Late training: low learning rate for precise optimization")
print("  ✓ Suppress overfitting and improve convergence")

Output:

=== Learning Rate Scheduler ===

1. CosineAnnealingLR
   Decay learning rate with cosine function
   Initial learning rate: 1e-3
   Minimum learning rate: 1e-6
   Period: 50 epochs

2. ReduceLROnPlateau
   Reduce learning rate when validation accuracy stops improving
   Decay factor: 0.5
   Patience epochs: 3

3. OneCycleLR
   Increase then decrease learning rate gradually
   Maximum learning rate: 1e-3
   Total steps: 5000 (100 steps/epoch × 50 epochs)

Using learning rate schedulers:
  ✓ Early training: high learning rate for broad exploration
  ✓ Late training: low learning rate for precise optimization
  ✓ Suppress overfitting and improve convergence

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