Phase 0: Vision Foundations · ~75 minutes · Python

Transfer Learning & Fine-Tuning

Somebody else spent a million GPU hours teaching a network what edges, textures, and object parts look like. You should borrow those features before training your own.

What you will learn

Distinguish feature extraction from fine-tuning and pick the right one based on dataset size, domain distance, and compute budget
Load a pretrained backbone, replace its classifier head, and train only the head to a working baseline in under 20 lines
Progressively unfreeze layers with discriminative learning rates so early generic features get smaller updates than late task-specific ones
Diagnose the three common failures: feature drift from too-high LR on unfrozen blocks, BN statistics collapse on tiny datasets, and catastrophic forgetting

Introduction

Type: Build
Languages: Python
Prerequisites: Image Classification
Time: ~75 minutes

Objective

Learning objectives

Distinguish feature extraction from fine-tuning and pick the right one based on dataset size, domain distance, and compute budget
Load a pretrained backbone, replace its classifier head, and train only the head to a working baseline in under 20 lines
Progressively unfreeze layers with discriminative learning rates so early generic features get smaller updates than late task-specific ones
Diagnose the three common failures: feature drift from too-high LR on unfrozen blocks, BN statistics collapse on tiny datasets, and catastrophic forgetting

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Key terms in this lesson

Fine-Tuning