Introduction

Data augmentation is often overlooked in favor of more complex model architectures. However, in my recent project involving agricultural disease detection, I found that smart augmentation was the key to success.

The Problem

We had a limited dataset of only 500 images per class. The model was overfitting severely.

The Solution

I implemented a pipeline using albumentations to generate:

• Rotations
• Color jitter
• Cutout revisions

"Data is the new oil."

Results

After training on the augmented dataset, validation accuracy jumped from 72% to 91%.