The primary aim of the project is to create an intelligent image recognition system capable of accurately identifying and categorizing objects within images. Image recognition has become an integral part of many technological applications—from enabling self-driving cars to interpret their surroundings to helping social media platforms tag people in photos. This project will utilize CNNs to train a model on labeled image datasets, allowing it to learn meaningful features and make predictions. The goal is for students to understand the complete process of building an image classification pipeline—from data collection and preprocessing to model training, evaluation, and interface development. By the end of the project, students are expected to produce a working prototype that can recognize and label images into predefined categories with high accuracy, demonstrating their grasp of key machine learning and computer vision concepts.
To complete the project, students will follow a systematic, twelve-week schedule. In the first week, they will be introduced to the theoretical foundation of Convolutional Neural Networks and their role in image recognition tasks. The second week involves preparing or collecting image datasets from reliable sources such as COCO or OpenCV. Next, students will explore popular deep learning libraries such as TensorFlow and PyTorch, which are essential for building and training CNN models.
Once familiar with the tools, students will develop the base framework of their model and begin training it using preprocessed datasets. This will be followed by evaluating the model’s accuracy using performance metrics like precision, recall, and confusion matrices. After iterative improvements to enhance model performance, students will integrate their trained model with a simple interface that allows users to input images and view recognition results. The final phases of the project include comprehensive testing, preparing technical documentation, and delivering a team presentation. During this entire process, students must maintain good coding practices, follow documentation standards, and ensure their work complies with academic integrity guidelines. Hardware requirements include a well-equipped PC or laptop with open-source software installed and, optionally, a basic camera module for image input.
