PineAIpple

This project is focused on building a deep learning model to classify fruits based on images using Convolutional Neural Networks (CNNs).

Dataset

The dataset used in this project is the Fruits-360 dataset. It contains images of various fruits that are grouped into different categories. The dataset is divided into three folders:

• Training data: Contains images for training the model.
• Validation data: Used to validate the model during training.
• Test data: Used to evaluate the model after training.

You can download the full dataset from Fruits-360 Dataset on Kaggle.

Project Structure

fruit-classification/
│
├── data/
│   ├── train/       # Training images
│   ├── val/         # Validation images
│   ├── test/        # Test images
│
├── notebooks/
│   └── fruit.ipynb
│   └── pineaipple-train.ipynb
│
├── src/
│   ├── data_preprocessing.py   # Data augmentation and preprocessing
│   ├── model.py                # Define the CNN model
│   ├── train.py                # Train the model
│   ├── evaluate.py             # Evaluate the model
│
├── models/
│   └── fruit_classifier_final.keras
│
├── app.py
├── requirements.txt
└── README.md

Project Overview

In this project, we:

• Preprocess and augment the images using TensorFlow’s ImageDataGenerator.
• Define a Convolutional Neural Network (CNN) model for image classification.
• Train the model using the training and validation sets.
• Evaluate the model using the test set to measure its accuracy.
• Visualize results including prediction examples.

Steps

1. Data Preprocessing:

   • Augment the images (rotate, shift, zoom, etc.) to increase dataset variety.
   • Split the data into training, validation, and test sets.

2. Model Definition:

   • A CNN model is built using TensorFlow/Keras layers to recognize fruit images.

3. Training:

   • The model is trained using the augmented data and validation sets.
   • Early stopping and model checkpointing are used to avoid overfitting.

4. Evaluation:

   • The model is evaluated using the test dataset, and performance metrics are displayed.

5. Prediction Visualization:

   • Predictions are made on test images, and the results are visualized.

Technologies Used

• Python: Programming language used for the project.
• TensorFlow/Keras: For building and training the CNN model.
• Matplotlib/Seaborn: For data visualization (plots and graphs).
• Scikit-learn: For additional evaluation metrics like classification report.

Requirements

• Python 3.x
• TensorFlow
• Keras
• Matplotlib
• Seaborn
• Scikit-learn

Install Requirements

To install the required dependencies, run:

pip install -r requirements.txt