With neural networks quickly being adopted in throughout society, understanding their behavior is becoming more important than ever. However, today's explainable AI field mostly consist of methods to explain single decisions of a model, which do not give us insight into the model as a whole, rendering the notion of explainability ambiguous. To this end, we contribute to the discussion of the distinction between explanation methods and explainability, and introduce Object-based Explainability (ObEy), a novel metric to quantify the explainability of models. ObEy is grounded in the natural in natural sciences and scores saliency maps based on visual perception of objects using segmentation masks. However, as such masks are not readily available in practical settings, we propose to use a new foundational model to generate segmentation masks, making our metric applicable in any setting. We demonstrate ObEy's immediate applicability to practical use cases, and present new insights into the explainability of adversarially trained models from a quantitative perspective.