Recent advancements in computer vision have led to human-level performance in image recognition tasks, but challenges persist in real-world applications due to differences in data distribution. This paper introduces CLARA, a semi-automatic framework designed to continually retrain computer vision models by incorporating expert annotation of new data. The system was tested in a production environment with a waste image classification model and showed improved performance and robustness against evolving data distributions. This approach encourages collaboration among stakeholders in waste management and utilizes human-in-the-loop strategies to enhance model accuracy. The framework is adaptable to various models and tasks and, additionally, supports fusion techniques that combine outputs from multiple models to improve prediction robustness and overall system reliability.