Can Class Names Forecast Per-Class Accuracy for Data-Free CLIP Customization?

Data-free customization trains a compact student from class names alone, but gives no per-class report when training finishes. We study a cheap way to estimate that gap without any real image of the target vocabulary. Our model forecast from name and inversion-based statistics targets student rather than teacher per-class accuracy on a data free customization system. We evaluate it under two protocols within and between classes. Scoring takes seconds once a synthetic pool exists; the estimate is coarse, Spearman $r_s$ trailing a labeled teacher oracle on global rank for three of four vocabularies under both protocols. But on compositional and fine-grained name lists it is even better than estimation from teacher with real labeled data: class-split worst $30\%$ precision beats the oracle on Flowers-102 and FGVC-Aircraft, and CUB-200 reaches $0.46$ leave-one-out global rank without target photographs. However, Our model cannot replace labeled evaluation when real images are provided, but it is a cheap per-class estimate that requires no real images.