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Fruit Image Classification

Overview

This project implements an advanced image classification solution using a pretrained MobileNetV2 model to accurately classify images of fruits. The solution encompasses comprehensive data preprocessing, model training, and prediction capabilities.

Project Structure

fruit-image-classification/
│
├── data/
│   ├── archive (6).zip
│   └── MY_data
|     ├── train/           # Training images
│     ├── test/            # Testing images
│     └── prediction/      # Images for prediction
│
├── src/
│   ├── preprocessing.py     # Dataset preprocessing and visualization
│   ├── model_training.py    # Model training script
│   ├── classify.py          # Prediction script
│   └── config.py            # Centralized configuration
│
├── results/              # Output storage
|   ├── plots/
|   |    └── sample_plot.png
│   ├── model_architecture.json
|   └── classifier_history.json
│
├── trained models/
│   └── fruit_classifier_model.h5
│
├── requirements.txt
└── README.md

Key Features

  • Pretrained Model: Leverages MobileNetV2 for robust fruit image classification
  • Advanced Preprocessing: Comprehensive image normalization
  • Training Optimization:
    • Early stopping callback
    • Performance monitoring
  • Visualization Tools: Dataset sample plotting
  • Flexible Prediction: Real-time image classification script

Installation

Prerequisites

  • Python 3.8+
  • pip package manager

Setup Steps

  1. Clone the Repository

    git clone https://github.com/Nirikshan95/FruitClassifier.git
cd FruitClassifier

  2. Install Dependencies

    pip install -r requirements.txt

  3. Prepare Dataset

    • Download "Fruit Classification (10 Class)" dataset from Kaggle
    • Place archive (6).zip in the data/ directory
    • The preprocessing script will handle extraction

Usage

1. Train the Model

python src/model_training.py

2. Predict New Images

python src/classify.py

Note: Update demo_img path in classify.py with your image

3. Visualize Dataset

python src/preprocessing.py

Configuration

The config.py file centralizes project configurations:

# Paths
DATA_DIR = "./data"
ZIP_FILE_PATH = "./data/archive (6).zip"
TRAINING_DATA = "./data/MY_data/train/"
TESTING_DATA = "./data/MY_data/test/"
PREDICTION_DATA = "./data/MY_data/prediction/"
MODEL_SAVE_PATH = "./trained models/fruit_classifier_model.h5"
MODEL_HISTORY_PATH = "./results/classifier_history.json"
MODEL_ARCHITECTURE_PATH="./results/model_architecture.json"
SAMPLE_PLOT_PATH="./results/plots/sample_plot.png"

# Hyperparameters
BATCH_SIZE = 32
EPOCHS = 10
OPTIMIZER = "adam"

# Model Parameters
INPUT_SHAPE = (224,224,3)
ACTIVATION_FUNCTION = "relu"

Results

  • Trained Model: fruit_classifier_model.h5
  • Model Architecture: results/model_architecture.json
  • Performance Metrics:
    • Training accuracy
    • Validation accuracy

Future Roadmap

  • Expand fruit class diversity
  • Fine-tune model architecture
  • Implement advanced data augmentation
  • Add model interpretability features

References

  1. Kaggle Fruit Classification Dataset
  2. MobileNetV2 Research Paper

Contributions

Contributions, issues, and feature requests are welcome!