Fruits Recognition Using Deep Learning with Data Augmentation

A deep learning project for classifying fruit images using CNNs, custom data augmentation, and transfer learning. Built on the Fruits-360 dataset, this project focuses on improving robustness with background replacement, noise, blur, and attention mechanisms.

Overview

Trained and evaluated the following models:

• EfficientNetB0
• ResNet50
• MobileNetV2

Dataset

• Fruits-360: 130+ fruit classes with uniform backgrounds
• All images resized to 100x100
• Split into training, validation, and test sets

Key Features

• Transfer Learning using pretrained CNNs
• Background replacement with random textures to simulate real-world conditions
• Extra augmentations: blur, noise, flips, zoom
• SE block to enhance channel-wise attention
• Early stopping and model checkpointing
• Detailed evaluation: classification reports, confusion matrices

Training Process