📌 Quality of Labeled Data

Data annotation is a labor-intensive and time-consuming process, heavily reliant on the expertise and precision of annotators. Since the quality of data annotations directly affects the performance of AI models, ensuring high-quality annotations is a critical task. As AI moves toward real-world applications across various vertical domains, professional datasets have become highly sought after to train or fine-tune models for specific industries.

However, the shortage of high-quality professional datasets is largely due to the fact that general annotators lack the specialized knowledge required for accurate annotation in fields such as medicine, biology, mechanics, and industry. Additionally, there is a significant lack of professional annotators in these fields. The path to improving annotation quality lies in providing better training, leveraging intelligent annotation assistance tools, and implementing rigorous quality control measures.