Data augmentation is a technique used in deep learning to increase the diversity of training data wout collecting new data. It helps imprope model generalization and reduces overfitting. This guide coves common strategies and calculations endived in data augmentation.

Common Data Augmentation Strategies

Several techniques are used to augment data, especially in imaxe procesing tasks. These Methods modifiy existing data to create new, varied samples for traing.

  • CLANE1; CLANE1; FLT: 0 CLANE3; CLANE3; Rotation: CLANE1; CLANE1; CLANE1; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CLANE1; CLANE1; CLANE1; CLANE1; CLANE1; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3; CCANEX images by a certain dixe range.
  • CLANE1; CLANE1; FLT: 0 CLANE3; CLANE3; Scaling: CLANE1; CLANE1; FLT: 1 CLANE3; CLANE3; CLANE3; Resizing images while maintaineg aspect ratio.
  • CLANE1; CLANE1; FLT: 0 CLANE3; CLANE3; Flipping: CLANE1; CLANE1; CLANE1; CLANE3; CLANE3; CLANE3; CLANE3; CLANE3OR vertical flips.
  • CLAS1; CLAS1; CLAS3; CLAS3; CLAS3; CLAS1; CLAS1; CLAS1; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; Changing brightness, contratt, or satution.
  • CLAS1; CLAS1; CLAS1; CLAS3; CLAS3; CLAS1; CLAS1; CLAS1; CLAS1; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS1; CLAS1; CLAS1; CLAS1; CLAS3; CLAS3; CLAS3; CLAS3; Random or centr cropping to focus on different pars of thee imaxe.

Kalkulace for Data Augmentation

Implementing data augmentation entrives calculating thoe probability and extent of transformations to ensure diversity wout compromising data integrity.

For exampla, if appliying rotation with in a range of 0 ° to 30 °, thee average rotation angle can be calculated as:

CLAS1; CLAS1; CLAS1; CLAS3; CLAS3; CLAS3; CLAS3; CLAS1; CLAS1; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; CLAS3; = (Minimum + Maximum) / 2 = (0 ° + 30 °) / 2 = 15 °

Equiarly, when appliying random cropping, thee crop size can be determinid based on then he original image size and desired coverage conditage.

Výhody of Data Augmentation

Using data augmentation techniques can lead to improvized model exposing thee model to a wider variety of data. It also helps in reducing overfitting, especially when training data is limited.