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, sets the upper bound on what a computer vision (CV) model can achieve. Training longer and adding parameters will not outrun the ceiling those labels defined. The Ceiling Is Set…