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In recent years, significant progress has been made in the research of faciallandmark detection. However, few prior works have thoroughly discussed aboutmodels for practical applications. Instead, they often focus on improving acouple of issues at a time while ignoring the others. To bridge this gap, weaim to explore a practical model that is accurate, robust, efficient,generalizable, and end-to-end trainable at the same time. To this end, we firstpropose a baseline model equipped with one transformer decoder as detectionhead. In order to achieve a better accuracy, we further propose two lightweightmodules, namely dynamic query initialization (DQInit) and query-aware memory(QAMem). Specifically, DQInit dynamically initializes the queries of decoderfrom the inputs, enabling the model to achieve as good accuracy as the oneswith multiple decoder layers. QAMem is designed to enhance the discriminativeability of queries on low-resolution feature maps by assigning separate memoryvalues to each query rather than a shared one. With the help of QAMem, ourmodel removes the dependence on high-resolution feature maps and is still ableto obtain superior accuracy. Extensive experiments and analysis on threepopular benchmarks show the effectiveness and practical advantages of theproposed model. Notably, our model achieves new state of the art on WFLW aswell as competitive results on 300W and COFW, while still running at 50+ FPS.

When Liebig's Barrel Meets Facial Landmark Detection: A Practical Model