Project Overview: During my 3rd semester, my colleagues and I developed a Bird Species Classification Application using Convolutional Neural Networks (CNN). This application was designed to accurately classify bird images into five distinct species: Broadbill, Eagle, Owl, Parrot, and Woodpecker. The image dataset consisted of 560 images, with 420 images used for training and 140 for testing, ensuring a balanced and comprehensive approach to model development and evaluation.
Technologies Used: Convolutional Neural Networks (CNN), Python, Jupyter Notebook, TensorFlow, Keras, Matplotlib, NumPy, Tkinter, Pygame
Key Features and Achievements
Machine Learning Implementation: Built and optimized a CNN model to classify bird species with high accuracy based on the provided image data.
Python & Deep Learning Integration: Combined Python with TensorFlow and Keras to design, train, and fine-tune the model, improving its performance over multiple iterations.
User Interface Development: Developed an intuitive interface using Tkinter, allowing users to upload images and view classification results instantly.
Image Augmentation: Applied techniques such as rotation, flipping, and scaling to augment the training dataset, enhancing model robustness and generalization.
Model Evaluation: Assessed model performance using metrics like accuracy, precision, and recall, and visualized results with Matplotlib to identify areas for improvement.
This project significantly enhanced my understanding of machine learning, particularly in image classification using CNNs and deep learning frameworks like TensorFlow and Keras. I also gained hands-on experience in Python programming, data visualization, and UI development, which strengthened my technical proficiency and problem-solving skills.
- Input of choosing an image.
- Input of uploading an image
- Output of the image