Why Your Model Only Learns What the Labels Teach It
A supervised model does not learn the world; it learns the labels a team of annotators assigned to pixels. If those labels disagree with each other, blur a category boundary, or skip the hard frames, the model absorbs that confusion as fact. This is why quality-focused data labeling, not the raw count of labeled images,…