The project focuses on enhancing Gluu's multi-modal image recognition model to improve the accuracy and efficiency of condition reporting for client products. The goal is to develop a custom Convolutional Neural Network (CNN) and integrate You Only Look Once (YOLO) object detection techniques to better identify and classify product conditions.

This project will allow learners to apply their knowledge of machine learning, computer vision, and neural networks to a real-world application. By focusing on CNN and YOLO, the project aims to create a robust system capable of handling diverse product images and providing precise condition assessments.

The project will involve tasks such as data preprocessing, model training, and performance evaluation, all of which are crucial for developing a reliable image recognition system.

The deliverables for this project include a fully functional CNN model tailored for condition reporting, integrated with YOLO for object detection. Additionally, a comprehensive report detailing the model's architecture, training process, and performance metrics will be provided. The project will also deliver a set of test cases and validation results to demonstrate the model's effectiveness in real-world scenarios.