This project involves developing an elephant image classification system using deep learning and transfer learning techniques. The objective is to train a model that learns visual features from elephant images and predicts the corresponding class based on the categories available in the training dataset.
The implementation follows a structured machine learning workflow, including business problem identification, dataset understanding, image preprocessing, baseline model building, and model training. Transfer learning is subsequently explored through multiple experiments to evaluate its effectiveness for the classification task. Different training configurations are compared to assess model performance and identify a suitable approach.
The trained model is integrated into a Streamlit-based web application, allowing users to submit elephant images and obtain classification predictions. The application is then deployed on an AWS EC2 instance, demonstrating the practical deployment of a deep learning model in a cloud environment.
The project provides hands-on experience in wildlife image classification, computer vision, transfer learning, model experimentation, interactive application development, and cloud deployment.