Elephant vs. Zebra Image Classification Project

Welcome to the Elephant vs. Zebra Image Classification Project README! In this project, I will be training a machine learning model to differentiate between images of elephants and zebras. The goal of this project is to develop a robust image classification system that can accurately distinguish between these two majestic creatures.

Authors:Eugene Kuloba

Table of Contents

• Project Overview
• Data Sources
• Getting Started
• Data Preprocessing
• Model Selection
• Model Training
• Model Evaluation
• Inference
• Conclusion
• Future Work
• For More Information
• Repository Structure

Project Overview:

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Image classification is a fundamental task in computer vision that involves assigning labels to images based on their content. In my project, I am focusing on distinguishing between two classes: elephants and zebras. This task has real-world applications in wildlife conservation, photography, and more.

Data Sources:

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Data was retrieved from the Data Repository for the [University of Minnesota], (https://doi.org/10.13020/D6T11K), under a creative commons license, from a study titled: Camera Trap Images used in "Identifying Animal Species in Camera Trap Images using Deep Learning and Citizen Science". All images were downloaded from Zooniverse and have been resized to 330x330 pixels.

Getting Started:

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To begin working with this project, follow these steps:

1. Clone this repository:

git clone https://github.com/eugenekuloba/animal-classification

2. Navigate to the project directory:

cd elephant-zebra-classification

3. Set up your Python environment and install dependencies:

pip install -r requirements.txt

4. Download the dataset and place it in the appropriate directory (data/) following the provided structure.

Data Preprocessing:

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Data preprocessing is a critical step in any machine learning project. We will perform the following preprocessing steps:

• Resize images to a consistent size.
• Normalize pixel values.
• Augment the training data with techniques such as random flips and rotations.

Model Selection:

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Model Training:

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Model Evaluation:

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Inference

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Conclusion

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Future Work

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Contributing

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I welcome contributions to this project! If you find any issues or have ideas for improvements, please submit a pull request or open an issue in the repository.

Repository Structure

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├── .gitignore
├── README.md
├── elephants_vs_zebras_photos.npy
├── index.ipynb
├── requirements.txt
└──