Résumé:
The image augmentation algorithm was used to increase the number of certain
images due to the unbalanced dataset and to increase the robustness of the model.
In this project, CycleGAN, one of the conditional GAN models, was used to
augment image data for disease detection. We have proposed to use a variant
that allows image-to-image translation without the need for paired examples of
transformation from source domain to target domain. It can transform the image
from one domain to another without one-to-one mapping between source domain
and target domain. The tests carried out on the Plantvillage dataset gave very
satisfactory results.
