Deep Learning in Data Science — Final Project

This repository contains the final project for the course DD2424 - Deep Learning in Data Science at KTH Royal Institute of Technology.

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Project Overview

The project implements a deep learning model based on the AlexNet architecture using TensorFlow to perform image classification on the Oxford 17 Category Flower Dataset and the Oxford 102 Category Flower Dataset.

The goal is to demonstrate convolutional neural networks' ability to classify fine-grained categories of flowers with high accuracy by leveraging transfer learning and data augmentation techniques.

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Datasets

• Oxford 17 Category Flower Dataset
• Oxford 102 Category Flower Dataset

Both datasets consist of high-quality images with detailed annotations for multiple flower categories.

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Architecture

• Model: AlexNet CNN architecture
• Framework: TensorFlow 2.x
• Key features:
  • Multiple convolutional layers with ReLU activations
  • Max-pooling layers for downsampling
  • Fully connected dense layers for classification
  • Dropout regularization to reduce overfitting

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Usage

Installation

Make sure you have Python 3.7+ and TensorFlow installed. It is recommended to use a virtual environment.

pip install -q -U tensorflow_hub
pip install -q tfds-nightly tensorflow matplotlib
pip install tflearn

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Training & Evaluation

To train the AlexNet model on the datasets, see the code under the Project folder:

• 17 category dataset
• 102 category dataset