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Facial landmark detection is a crucial preprocessing step in many applications that process facial images. Deep-learning-based methods have become mainstream and achieved outstanding performance in facial landmark detection. However, accurate models typically have a large number of parameters, which results in high computational complexity and execution time. A simple but effective facial landmark detection model that achieves a balance between accuracy and speed is crucial. To achieve this, a lightweight, efficient, and effective model
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