  Real-time object detection is significant for industrial and research fields.
On edge devices, a giant model is difficult to achieve the real-time detecting
requirement and a lightweight model built from a large number of the depth-wise
separable convolutional could not achieve the sufficient accuracy. We introduce
a new lightweight convolutional technique, GSConv, to lighten the model but
maintain the accuracy. The GSConv accomplishes an excellent trade-off between
the accuracy and speed. Furthermore, we provide a design suggestion based on
the GSConv, Slim-Neck (SNs), to achieve a higher computational
cost-effectiveness of the real-time detectors. The effectiveness of the SNs was
robustly demonstrated in over twenty sets comparative experiments. In
particular, the real-time detectors of ameliorated by the SNs obtain the
state-of-the-art (70.9% AP50 for the SODA10M at a speed of ~ 100FPS on a Tesla
T4) compared with the baselines. Code is available at
https://github.com/alanli1997/slim-neck-by-gsconv
