What is data labeling & annotation?

Annotating large datasets is resource-intensive, especially when dealing with complex tasks like semantic segmentation or 3D object tracking. Scaling the annotation process while maintaining quality is a key challenge.

Human annotators must be consistent in their labeling, as even minor variations can affect model performance. This requires thorough training programs and continuous quality checks to minimize errors.

Handling sensitive data, such as medical records or personal information, requires compliance with privacy regulations and secure infrastructure. Annotation platforms must implement robust security measures to protect data integrity.

Annotated data can inadvertently introduce biases into models. It’s crucial to have different teams of annotators and comprehensive guidelines to minimize biases and ensure fair representation across data samples.

Defining a clear and consistent taxonomy for labeling tasks makes sure annotators understand the categories and attributes they need to apply. This is especially important for complex applications such as medical imaging or autonomous driving.

Implementing multilevel quality checks such as edit review workflows, consensus models, and sample reviews can significantly improve annotation quality. Automated quality checks powered by machine learning can also identify discrepancies and flag errors in real time.

Using annotation platforms like Uber’s uLabel and uTask can streamline workflows. These platforms offer features like automated pre-labeling, customizable UI configurations, and real-time analytics to manage large-scale annotation tasks efficiently.