Nuclei detection in cervical cytology images holds significant promise for enhancing the early identification of cervical cancer. In this study, we apply GravityNet, a one-stage end-to-end detector designed ad hoc for detecting small regions, like cervical cell nuclei. This approach is based on pixel-based anchors, which serve as reference points on the image pixel grid and dynamically move towards the suspicious regions by inspecting the surrounding space. We conducted experiments on the publicly available Cervix93 dataset to evaluate the effectiveness of our method. Despite challenges posed by variations in staining techniques and cell morphology, our results show promising performance compared to the current state-of-the-art methods.