  This paper tackles the problem of object counting in images. Existing
approaches rely on extensive training data with point annotations for each
object, making data collection labor-intensive and time-consuming. To overcome
this, we propose a training-free object counter that treats the counting task
as a segmentation problem. Our approach leverages the Segment Anything Model
(SAM), known for its high-quality masks and zero-shot segmentation capability.
However, the vanilla mask generation method of SAM lacks class-specific
information in the masks, resulting in inferior counting accuracy. To overcome
this limitation, we introduce a prior-guided mask generation method that
incorporates three types of priors into the segmentation process, enhancing
efficiency and accuracy. Additionally, we tackle the issue of counting objects
specified through text by proposing a two-stage approach that combines
reference object selection and prior-guided mask generation. Extensive
experiments on standard datasets demonstrate the competitive performance of our
training-free counter compared to learning-based approaches. This paper
presents a promising solution for counting objects in various scenarios without
the need for extensive data collection and counting-specific training. Code is
available at \url{https://github.com/shizenglin/training-free-object-counter}
