High-quality datasets are essential for training reliable and accurate machine learning models. Poor dataset quality—such as blur, dark, or bright images—can significantly impact performance, leading to biased predictions, reduced model accuracy, and suboptimal decision-making.

Identifying and addressing these quality issues ensures that datasets remain clean, representative, and effective for AI applications. Visual Layer provides advanced tools to automatically detect and manage dataset quality issues, helping users streamline preprocessing, reduce errors, and optimize model training.

This section covers common dataset quality issues and how to quickly detect and remove them with One Click:

  • Detect and Remove Dark images & Objects
  • Detect and Remove Blur images & Objects
  • Detect and Remove Bright images & Objects

Each issue provides tools to filter by IS (show affected items) and IS NOT (exclude affected items) to help you clean your dataset effectively.