oxford-flowers-image-classifier

Goal

The goal of the project is to train an image classifier to recognize different species of flowers.

Process

• Load the oxford flowers image dataset and create a pipeline.
• Build and Train an image classifier on this dataset using transfer learning with mobilenet_v2 as the base pre-trained network from TensorFlow Hub.
• Save and use the trained model to perform inference on unseen flower images.

Results

• After training the model for 5 epochs, we achieved a training accuracy of 98.63% and an accuracy of 74.695% on the testing set.
• The model was saved and later used in a command line script and a Flask web application to predict the species of flowers from random images. The web app can be found at https://oxford-flowers.azurewebsites.net

Install

This project requires Python 3.x and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• Tensorflow 2.9

You will also need to have software installed to run and execute an iPython Notebook

Code

Code is provided in the Project_Image_Classifier_Project.ipynb notebook file.

Run

In a terminal or command window, navigate to the top-level project directory (that contains this README) and run one of the following commands:

ipython notebook Project_Image_Classifier_Project.ipynb

or

jupyter notebook Project_Image_Classifier_Project.ipynb

This will open the iPython Notebook software and project file in your browser.

Data

The dataset consists of the following images downloaded using tensorflow_datasets module:

• https://www.tensorflow.org/datasets/catalog/oxford_flowers102

Features

• Features are defined as the colors of the pixels in the imae(s).