Data refinement strategies are crucial in improving the quality of data used to train machine learning-based models in computer vision. These strategies aim to identify and correct errors, outliers, and biases in the data, which can significantly impact the performance of the model. The two main paradigms in computer vision - model-centric and data-centric - differ in their approach to achieving this objective. Model-centric focuses on developing complex machine learning models, while data-centric prioritizes high-quality data. Effective data refinement strategies include smart data sampling, improving data quality, improving label quality, finding model failure modes, active learning, semi-supervised learning, and refinement by image, class, or quality metrics. These strategies require high-quality data, accurate and consistent labels, and a deep understanding of the data and technology. By using these strategies, developers can achieve higher model accuracy and produce more reliable outcomes for their computer vision models.