Date Fruit Image Classification

Overview

This project classifies nine different types of date fruits using a Custom Convolutional Neural Network (CNN). The custom CNN is lightweight and efficient compared to larger architectures like EfficientNet-B0.

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Dataset

The dataset consists of 9 classes of date fruits:

• Ajwa
• Galaxy
• Mejdool
• Meneifi
• NabtatAli
• Rutab
• Shaishe
• Sokari
• Sugaey

Training Images: 1,156
Testing Images: 502

Images were preprocessed to size 224x224 with augmentation techniques such as:

• Random Horizontal Flip
• Random Rotation
• Normalization

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Model Architecture

The Custom CNN includes:

1. 4 Convolutional Blocks:
   • Convolutions → Batch Normalization → ReLU → Max Pooling
2. Fully Connected Layer with Dropout (0.5).
   Diagram generated using the PlotNeuralNet library

Model Summary:

• Total Parameters: 128,199
• Final Accuracy: 97% on the test dataset.

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Results

Custom Model vs EfficientNet-B0

Model	Parameters	Size (MB)	Accuracy (%)
Custom Model	128,199	30.22	97
EfficientNet-B0	4,019,077	124.56	100

Model Comparison

• Model Size: EfficientNet-B0 is 4.12 times larger than the Custom Model.
• Parameter Count: EfficientNet-B0 has 31.36 times more parameters.