Python Script and CNN Model for 2023/2024 IMLO Individual Assessment

This project implements a Convolutional Neural Network for classifying flower images from the Oxford Flowers-102 dataset using PyTorch.

It is strongly recommended to run the training program on a computer with a GPU.

Set up

1. Clone repository:

   git clone git@github.com:talaha3/IMLO-Coursework.git

2. Create the Python virtual environment:

   cd IMLOAssessment
   python -m venv myenv

3. Activating virtual environment:

   On Windows:

   ./myenv/Scripts/activate.bat

   On Mac and Linux:

   $ source myvenv/bin/activate

4. Install the required packages:

   pip install -r requirements.txt

Usage

Training the model using training dataset

python finalClassifier.py

Testing the train model on test dataset

python modelTest.py

Files

/attempts: Contains previous attempts at creating classifier.

finalClassifier.py: Trains the model and saves most accurate parameter configuration.

modelTest.py: Loads saved model and tests accuracy on the test dataset.

bestmodel.pt: Saved model with trained parameters yielding highest accuracy.

dataset_mean_std.py: Calculates and prints the mean and standard deviation of the training set.

requirements.txt: List of pip packages required to run the program.