Fine-Tuning CNNs
In the previous lesson, you learned what transfer learning is and why it works. Now we go one level deeper and understand fine-tuning — the technique that turns a generic pretrained model into a task-specific expert.
Fine-tuning is one of the most important skills in modern Computer Vision projects. It decides whether your model performs “okay” or performs really well.
What Does Fine-Tuning Mean?
Fine-tuning means:
Allowing selected layers of a pretrained CNN to update their weights during training.
Instead of freezing the entire network, we carefully decide which layers should learn and which layers should stay fixed.
This lets the model adapt its knowledge to your specific dataset.
Why Fine-Tuning Is Needed
Feature extraction works well when:
- Your dataset is small
- Your task is very similar to the pretrained task
But in many real-world problems:
- The image domain is different
- Lighting, texture, or structure changes
- Generic features are not enough
Fine-tuning solves this by letting the model adjust deeper representations.
Understanding CNN Layers Intuitively
Think of a CNN as learning vision in stages:
- Early layers: edges, corners, textures
- Middle layers: shapes, object parts
- Deep layers: object-level concepts
Fine-tuning focuses on the layers that are closest to the task meaning.
Which Layers Should Be Fine-Tuned?
There is no single correct answer, but there are strong guidelines.
| Situation | Recommended Strategy |
|---|---|
| Small dataset, similar task | Freeze most layers, fine-tune last layers |
| Medium dataset | Fine-tune top half of the network |
| Large dataset, different domain | Fine-tune most or all layers |
This balance prevents overfitting while still learning task-specific features.
Fine-Tuning vs Feature Extraction
Let’s clearly separate the two ideas.
| Aspect | Feature Extraction | Fine-Tuning |
|---|---|---|
| Model weights | Frozen | Partially trainable |
| Training speed | Fast | Slower |
| Risk of overfitting | Low | Higher (if not careful) |
| Accuracy potential | Moderate | Higher |
In practice, many projects start with feature extraction and later move to fine-tuning.
Learning Rate: The Most Critical Factor
Fine-tuning requires special care with learning rates.
Why?
Because pretrained weights already contain valuable knowledge. A large learning rate can destroy that knowledge very quickly.
General rule:
- Use a much smaller learning rate for fine-tuning
This allows gradual adaptation instead of abrupt changes.
Common Fine-Tuning Workflow
A standard fine-tuning process looks like this:
- Load pretrained CNN
- Replace the final classification layer
- Freeze early layers
- Unfreeze selected deeper layers
- Train with a low learning rate
This approach is widely used in industry projects.
When Fine-Tuning Can Hurt Performance
Fine-tuning is powerful, but not always safe.
It can reduce performance if:
- Dataset is extremely small
- Too many layers are unfrozen
- Learning rate is too high
That is why understanding comes before coding.
Is This Lesson Coding or Conceptual?
This lesson focuses on conceptual control and decision-making.
You are learning:
- Why fine-tuning works
- When to use it
- How to avoid common mistakes
Actual implementation will come in upcoming lessons where we apply these ideas carefully.
Practice Questions
Q1. What is fine-tuning in CNNs?
Q2. Why is a low learning rate important during fine-tuning?
Q3. Which layers are usually frozen?
Mini Assignment (Thinking Exercise)
Imagine you are building a model for:
- Medical scans
- Satellite images
- Retail product photos
Think about:
- How similar is the data to ImageNet?
- Which layers would you fine-tune?
This reasoning is more important than memorizing code.
Quick Recap
- Fine-tuning adapts pretrained models to new tasks
- Only selected layers should be trained
- Low learning rates are critical
- It improves performance when used correctly
Next lesson: Image Classification Pipeline — connecting data, model, and evaluation.