Input Image

(227 x 227 x 3)

Data Preprocessing

MinMax Normalization, Augmentation

Conv2D(8) → ReLU → BatchNorm → Max Pool

Conv2D(16) → ReLU → BatchNorm → Max Pool

Conv2D(32) → ReLU → BatchNorm → Max Pool

Conv2D(64) → ReLU → BatchNorm → Max Pool

Flatten Layers

Dense Layers → Dropout

Model training

Model Evaluation

Final Output