Why Your Model Only Learns What the Labels Teach It

Supervised computer vision models learn from the quality of labels assigned by annotators, not directly from the images. Flawed or inconsistent labels set a performance ceiling that cannot be overcome by more data or larger models. Research shows that correcting labels can drastically change model rankings, highlighting the critical role of label quality in model evaluation and deployment.