When Bigger is Worse: A Practitioner's Guide to Model Selection Under Data Scarcity
Scaling laws assume larger models trained on more data consistently outperform smaller ones -- an assumption that drives model selection in computer vision but remains untested in resource-constrained Earth observation (EO). We conduct a systematic efficiency analysis across three scaling dimensions: model size, dataset size, and input resolution, on rooftop photovoltaic (PV) detection in Madagascar, yielding 180 training runs across 60 configurations. Optimizing for model efficiency (mAP$_{50}$ per unit of model size), we find a consistent efficiency inversion: YOLO11N achieves the highest efficiency ($22\times$ higher than YOLO11X) with no accuracy penalty: it reaches the second highest...
| Authors | Kwame Mbobda-Kuate, Gabriel Kasmi |
|---|---|
| Primary category | cs.CV |
| Published | 2 March 2026 |
| Last updated at source | 17 July 2026 |
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