Training an image classifier in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main goal when training an image classifier?

The main goal is to teach the model to recognize and correctly label images based on their content by learning patterns from example images.

beginner

What role does the 'loss function' play during training?

The loss function measures how far the model's predictions are from the true labels. It helps the model learn by showing how to improve.

beginner

Why do we split data into training and validation sets?

We use the training set to teach the model and the validation set to check if the model is learning well and not just memorizing the training images.

intermediate

What is 'overfitting' in image classification training?

Overfitting happens when the model learns the training images too well, including noise, and performs poorly on new, unseen images.

beginner

Name a common metric used to evaluate image classifier performance.

Accuracy is a common metric; it shows the percentage of images the model correctly labels out of all tested images.

What does the training process of an image classifier mainly involve?

A Adjusting model weights to reduce prediction errors

B Collecting more images without labeling

C Randomly guessing image labels

D Deleting images from the dataset

Training adjusts the model's internal settings (weights) to make better predictions by reducing errors.

Why is a validation set important during training?

A To label images automatically

B To test the model on new data and check for overfitting

C To speed up the training process

D To increase the size of the training data

The validation set helps check if the model can generalize well beyond the training images.

Which metric tells you how many images were correctly classified?

A Loss

B Learning rate

C Accuracy

D Epoch

Accuracy measures the percentage of correct predictions out of all predictions.

What happens if a model overfits during training?

A It ignores the training data

B It performs well on new images

C It stops training automatically

D It performs poorly on new images

Overfitting means the model memorizes training data and fails to generalize to new images.

Which of these is NOT part of training an image classifier?

A Ignoring the labels

B Adjusting model parameters

C Evaluating model performance

D Feeding labeled images to the model

Ignoring labels would prevent the model from learning correct image categories.

Explain the steps involved in training an image classifier from start to finish.

Think about how the model learns and how we check its progress.

Describe what overfitting is and how it affects an image classifier's performance.

Consider what happens when a student memorizes answers instead of understanding.