This project explores image classification by training a model to identify different types of animals from images. The main aim is to refresh my Python skills while building a practical machine learning project.
Through this project, I will work through the full image classification pipeline, from downloading a dataset to training and improving a model. The project will also help me strengthen my understanding of how image datasets are structured and how machine learning models learn from visual data.
The main goals of this project are to:
- download and work with image datasets from Kaggle
- explore the dataset structure and understand how image data is stored
- clean and inspect the data before training
- practise using Python libraries commonly used in machine learning projects
- build a classification model to identify different animals from images
- improve the model over time to reduce training and validation loss
- Python
- pathlib
- os
- matplotlib
- torchvision
- PyTorch
- Download the dataset from Kaggle
- Explore the classes and image folders
- Inspect image samples and class balance
- Prepare the dataset for training
- Build a classification model using PyTorch
- Train and evaluate the model
- Improve performance through experimentation and tuning
The purpose of this project is both educational and practical. It allows me to revisit core Python concepts such as file handling, loops, and working with libraries, while also developing a better understanding of machine learning and image classification.