ehvenga / flower.identification-tensorflow

This Jupyter Notebook presents a TensorFlow-based model to classify flower species from images using a convolutional neural network (CNN). The notebook covers data preprocessing, model training, and performance evaluation.

Flower Species Classification using TensorFlow

Overview

This repository contains a Jupyter Notebook that demonstrates the use of a convolutional neural network (CNN) to classify flower species from images. The model is built using TensorFlow and covers the complete workflow from data preprocessing to model training and performance evaluation.

Prerequisites

• Python 3.x
• TensorFlow 2.x
• NumPy
• Matplotlib (for visualization)

Installation

Clone this repository to your local machine using:

git clone https://github.com/your-username/flower-classification.git

jupyter notebook Flower_Classification.ipynb

Dataset

The dataset used in this notebook includes various flower species and is preprocessed for optimal model training performance.

Model

The CNN architecture defined in the notebook is structured to efficiently learn the distinguishing features of different flower species based on their images.

Evaluation

The notebook includes sections for evaluating the model's accuracy on test data, providing insights into its performance.