High-quality datasets are essential for training reliable and accurate machine learning models. Poor dataset quality—such as blurry, dark, or overly 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 explores common dataset quality issues and how to effectively mitigate them:

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