Color Jittering

A collection of data examples used for training, validating, or testing machine learning models.

The examples a model learns its weights from, kept separate from the validation and test data used to check how well it generalizes.

When a model fits its training data too closely, noise included, so it scores well on examples it has seen and poorly on new ones.

Creating variations of training data through transformations (rotation, cropping, noise) to improve model generalization.

Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.

A subset of machine learning that uses neural networks with multiple layers (deep neural networks) to learn hierarchical representations of data.

The field of AI that gets computers to extract meaning from images and video: what is in them, where it is, and how it moves.

Applying transformations (rotation, flip, crop, color) to increase training data diversity.