https://github.com/mpoojithavigneswari/vegetable-classification-project

Created a website of vegetable classification using vgg16 CNN model with 99% accuracy.
https://github.com/mpoojithavigneswari/vegetable-classification-project

deep-learning keras neural-networks python streamlit tensorflow vgg16-model

Last synced: about 2 months ago
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Created a website of vegetable classification using vgg16 CNN model with 99% accuracy.

• Host: GitHub
• URL: https://github.com/mpoojithavigneswari/vegetable-classification-project
• Owner: MPoojithavigneswari
• Created: 2024-07-17T14:23:40.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-07-17T18:06:16.000Z (6 months ago)
• Last Synced: 2024-07-19T00:46:11.557Z (6 months ago)
• Topics: deep-learning, keras, neural-networks, python, streamlit, tensorflow, vgg16-model
• Language: Jupyter Notebook
• Homepage: https://vegetable-classification-project.streamlit.app/
• Size: 588 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Vegetable Classification Project

## Overview

This project involves creating a web application to classify vegetable images using a VGG16-based Convolutional Neural Network (CNN) model, achieving 99% accuracy. The application is built using Streamlit for the front-end interface. The process includes data extraction, data preprocessing, model building with the VGG16 CNN model, and model evaluation. The objective is to classify images of vegetables into one of 15 categories: Bean, Bitter Gourd, Bottle Gourd, Brinjal, Broccoli, Cabbage, Capsicum, Carrot, Cauliflower, Cucumber, Papaya, Potato, Pumpkin, Radish, and Tomato.

## Project Structure

- `VeggiesClassification_modelTraining.ipynb`: Jupyter notebook used to train the VGG16 model on the vegetable dataset.

- `veggiesClassification_model.keras`: Trained VGG16 model file.

- `streamlit_app.py`: Streamlit web application .py file.

- `requirements.txt`: List of Python packages required to run the application.

- `Examples_ss/`: Directory containing example screenshots of the web app.

## Installation

1. Clone the repository:

```bash

git clone https://github.com/MPoojithavigneswari/Vegetable-Classification-Project.git

```

2. Install the required packages:

```bash

pip install -r requirements.txt

```

## Usage

Run the Streamlit app:

```bash

streamlit run streamlit_app.py

```

The web application will open in your default web browser. You can upload an image of a vegetable either from your local system or by providing an image URL.

## Dataset

Dataset is collected from kaggle. click [here](https://www.kaggle.com/datasets/misrakahmed/vegetable-image-dataset) for dataset download

## Application Features

- **Image Upload:** Users can upload images of vegetables directly from their local machine.

- **URL Input:** Users can provide a URL to an image of a vegetable.

- **Top-3 Predictions:** The application displays the top-3 predicted classes along with their probabilities.

- **Responsive Design:** The application layout adjusts based on the screen size.

## Contributing

Contributions are welcome! Please fork the repository and create a pull request for any improvements or bug fixes.