Fine-tuning approach in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the fine-tuning approach in machine learning?

Fine-tuning is a method where a pre-trained model is adapted to a new, related task by continuing training on new data. It helps the model learn specific features of the new task while keeping general knowledge from before.

beginner

Why do we use fine-tuning instead of training a model from scratch?

Fine-tuning saves time and resources because it starts from a model that already knows useful features. It also often improves performance, especially when the new dataset is small.

intermediate

What does it mean to 'freeze' layers during fine-tuning?

Freezing layers means keeping some parts of the pre-trained model fixed so they don't change during training. This helps keep the general knowledge intact while only adjusting other parts for the new task.

intermediate

How does fine-tuning help in computer vision tasks?

In computer vision, fine-tuning allows models trained on large image datasets to adapt to specific tasks like recognizing new objects or styles, improving accuracy without needing huge new datasets.

intermediate

What is a common strategy for choosing which layers to fine-tune?

A common strategy is to freeze early layers that capture basic features like edges and textures, and fine-tune later layers that learn task-specific details.

What is the main benefit of fine-tuning a pre-trained model?

A It guarantees perfect accuracy

B It removes the need for any new data

C It trains the model from scratch

D It reduces training time and improves performance on new tasks

Fine-tuning uses existing knowledge to quickly adapt to new tasks, saving time and often improving results.

During fine-tuning, what does freezing layers do?

A Prevents some layers from updating weights

B Deletes those layers

C Adds new layers to the model

D Increases the learning rate

Freezing layers keeps their weights fixed so they don't change during training.

Which layers are usually fine-tuned in computer vision models?

A Early layers only

B Later layers that capture task-specific features

C Only the input layer

D All layers equally

Later layers learn details specific to the new task, so they are often fine-tuned.

Fine-tuning is especially useful when the new dataset is:

A Very small

B Very large

C Unrelated to the original task

D Perfectly labeled

Fine-tuning helps when new data is limited by leveraging prior knowledge.

What is a pre-trained model?

A A model with no training

B A model trained only on the new task

C A model trained on a large dataset before fine-tuning

D A model that cannot be fine-tuned

Pre-trained models have learned general features from large datasets and can be adapted to new tasks.

Explain the fine-tuning approach and why it is useful in computer vision.

Think about how a model trained on many images can help with a new image task.

Describe the role of freezing layers during fine-tuning and how to decide which layers to freeze.

Consider which parts of the model learn basics vs. details.