ScrewCount: A Dataset and Benchmark for Exemplar Efficiency and Text-Guided Few-Shot Object Counting

Abstract

General object detection methods struggle to detect large numbers of small, overlapping objects, such as screws and nuts, in industrial inspection. Moreover, creating dense annotations in these applications is difficult and costly, motivating the need for few-shot object counting approaches that can generalize with minimal supervision. While methods like Learning to Count Everything and CountGD have achieved progress, the interaction between exemplar efficiency, exemplar robustness, and text guidance remains unknown. In this paper, we present ScrewCount, a new dataset for dense small-object counting in manufacturing contexts. Using ScrewCount, we conduct a systematic study of exemplar selection, analyzing how the number and quality of exemplars affect few-shot counting performance. Our experiments show diminishing returns beyond a small number of exemplars and sensitivity to annotation noise. We further evaluate a text-guided counting method, examining the influence of prompt phrasin g on the results. Findings reveal that while text offers flexibility, performance is highly dependent on the prompt design, significantly affecting the method’s performance in some cases. ScrewCount establishes a benchmark for dense small-object counting and provides new insights into exemplar efficiency, robustness, and text guidance under limited annotation.