Multiclass Image Classification

Made solely by Aevin Jais

Project Description

This project utilises the Keras sequential model to create a custom deep convoluted neural network in order to classify images into multiple classes. In this specific case, there are three classes: Cats, Dogs and Fish.

I utilised an initial model composed of 3 convolutional layers in order to create the model. Upon training for 10 epochs, the val_accuracy converged to 73%. This was a solid start.

From here I increased the complexity of the model and added more 2D convolutional layers. Initially, I trained for 10 epochs but noticed the val_accuracy was dropping around 8, indicating overfitting was occurring. So I reran on 8 epochs and got the following:

X-Axis: epochs | Y-Axis: accuracy (max is 1)

This change resulted in an improved accuracy of 76%.

After, this I altered the learning rate via my optimizer (Adam) from the default of 0.001 to 0.0001. Here are the results:

X-Axis: epochs | Y-Axis: accuracy (max is 1)

This change resulted in an improved accuracy of 81%.

However, I believe there to be some degree of overfitting being introduced even in this model given that the gradient of val_accuracy plateaus around epoch 6. Nevertheless, the val_accuracy of this model was 81% and my highest-achieving model.

Table of Contents

• Features
• Installation
• Usage
• Technologies Used
• Contributing
• License

Features

main.py offers the ability to:

• Create the model (given that you have the necessary libraries installed - and preferably have your GPU enabled for TensorFlow) (using the model template in the code of course)
• Build the model
• Train the model
• Evaluate the model
• Test on data that is unseen to the model
• Load an existing model (avoids the need to create a model)

Installation

Python 3.9.4 was used to run this code. I suggest you use Python 3.9. as well**

There is a requirements.txt file in the root directory. Use this along with pip in order to install the necessary libraries:

pip install -r requirements.txt

NOTE: requirements.txt will not enable GPU usage. You have to do that yourself. Read below:

This project was made on my laptop. I have a mobile RTX 3060, therefore I was able to train my models in minutes as opposed to hours. If you do not configure GPU usage or have a GPU usage the code will still work but just slowly.

To enable GPU usage, follow this guide on installing WSL on Windows machines: https://www.tensorflow.org/install/pip#step-by-step_instructions

Once installing WSL, you will need to run the following lines:

conda activate tf
export XLA_FLAGS=--xla_gpu_cuda_data_dir=/usr/lib/cuda

Usage

To be implemented

Technologies Used

Language:

Python

Libraries:

• TensorFlow
• matplotlib
• cv2
• numpy

Contributing

If you're open to contributing to this project please contact me via email: [email protected].

License

None

Contact Information

Email: [email protected] IG: aevin.j

Acknowledgments

@NicholasRenotte @KGPTalkie @SimplilearnOfficial