Dozens of saliency models have been designed over the last few decades, targeted at diverse applications ranging from image compression and retargeting to robot navigation, surveillance, and distractor detection. Barriers to their use include the different and often incompatible software environments that they rely on, as well as the computational inefficiency of older implementations. For application-purposes models are then frequently chosen based on convenience and efficiency, at the expense of optimizing for task performance. To facilitate the evaluation and selection of saliency models for different applications, we present KDSalBox- a toolbox of 10 knowledge-distilled saliency models. Using the original model implementations available in their native environments, we produce saliency training data for efficient MobileNet-based architectures, that are identical in their architecture but differ in how they learn to distribute saliency over an image. The resulting toolbox allows these 10 models to be efficiently run, compared, and be practically applied.