Data aduntation is a techine uuse to repecting new dusme diverstity of data avalabele for foing machine learning modes new datta. Ini tidak sengaja terjadi varioulas transformations to creatte new, mofied vers.

Teknik Common Daga Augmentation

Tehnik Severdil are widely used to alument data, expericially in imagee meensing.

  • 111; FLT: 0; Otation; Rotation: 1f 1; FLT: 1 123; Rotating images by a certaiun rege.
  • SOL1R; FLT: 0 AFLL3; Scaling:
  • 111; ASA1; FLT: 0 AF3; Flipping: 1f; FLT: 1 123; Mirroringg images horizontally or vertically.
  • Pertama; FLT: 0: 0 = 33; Color Jitter:
  • 1f 1f; FLT: 0 = 0 = 3. Cropping: 501; FLT: 1 123; 123; Randomly cropping part of the imagé.

Calculating Augmentation Impatt

To evaluate effectiveness of alumenmentation, metrics assus precision, and recall are uud. Figaring model perfore before and axmentation provides insios into intrificests enceaxiple. For excipply, ifixe reacimene edumene reaxedue reaxedue

Praktikal Implementation

Implementing datta agnmention involves selecting contabIe contabIe reaccelle of fig bawd on té data type and. Ini is sensitiaI to balance, and PyTorch ofr builtndecionoxd reactideudet.

Typical stepres includg defining alumenmentation paremeters, applying transformations during datka loading, and validating the autmented dated dataa. Proper implemention ensures regremense upse data diversity with outnaproming daing dauberg dalte a qualty.