In this study, we introduce FEET, a standardized protocol designed to guide the development and benchmarking of foundation models. While numerous benchmarks exist for assessing these models, we propose a structured evaluation across three distinct scenarios to obtain a comprehensive understanding of their practical performance. We define three principal use cases: frozen embeddings, few-shot embeddings, and fully fine-tuned embeddings. Each scenario is detailed and exemplified through a case study in the medical domain, illustrating how these evaluations provide an extensive assessment of the effectiveness of foundation models in research applications. This protocol is recommended as a standard for ongoing research dedicated to representation learning models for deep learning research.