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🍎🍌 Rotten Fruit Classification - Image Recognition Project

Table of Contents

  1. Project Overview
  2. Features
  3. Project Structure
  4. Setup Instructions

Project Overview

This project aims to identify rotten fruits using image recognition techniques. The goal is to classify fruit images into categories such as fresh and rotten. This can have real-world applications in industries like agriculture and retail, where identifying spoiled produce efficiently is important.

Features:

  • Uses Convolutional Neural Networks (CNNs) for image classification.
  • Uses transfer learning to improve model accuracy.
  • Data preprocessing includes image augmentation (rotation, scaling, etc.) to improve model robustness.
  • Model is trained and tested on a dataset of various fruits in both fresh and rotten conditions.
  • Built with Pytorch.

Project Structure

  • data/fruits: Contains the training and testing datasets of fruits (fresh and rotten).

    • train/: Training images, labeled as fresh or rotten followed by the fruit name.
    • valid/: Test images for evaluation, same format as the training images.

  • models/: Pre-trained models saved after training.

  • notebooks/: Jupyter notebooks used for data exploration and initial model prototyping.

  • src/: Source code directory.

    • utils.py: Helper functions for the project.

  • README.md: Documentation file (you’re reading it now!).

Setup Instructions

1. Clone the repository:

git clone https://github.com/Hazim-T/Rotten_Fruits
cd Rotten_Fruits

2. Install required packages:

pip install -r requirements.txt