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…
This article has been indexed from Unite.AI
Read the original article: