Flower Classification CNN Model

This project implements a Convolutional Neural Network (CNN) model for classifying images of flowers into five categories: daisy, dandelion, rose, sunflower, and tulip.

Features

• Train a CNN model on a dataset of flower images
• Classify flower images using the trained model
• Real-time classification using webcam input
• Streamlit web application for easy interaction

Installation

1. Clone this repository: git clone (https://github.com/ennajari/Floral_Recognition) cd

2. Create a virtual environment (optional but recommended): python -m venv venv source venv/bin/activate # On Windows, use venv\Scripts\activate

3. Install the required packages: pip install -r requirements.txt

Usage

---> . To run the Streamlit web application: streamlit run app.py

Project Structure

• app.py: Main Streamlit application for flower classification
• Flower_Recog_Model.keras: Saved trained model
• Images/: Directory containing the dataset of flower images
• Sample/: Directory containing sample images for testing

Model Performance

The model achieves high accuracy on both training and validation sets. Refer to the generated plots for detailed performance metrics.

Contributors

Ennajari abdellah @ennajari