  Object detection is a basic but challenging task in computer vision, which
plays a key role in a variety of industrial applications. However, object
detectors based on deep learning usually require greater storage requirements
and longer inference time, which hinders its practicality seriously. Therefore,
a trade-off between effectiveness and efficiency is necessary in practical
scenarios. Considering that without constraint of pre-defined anchors,
anchor-free detectors can achieve acceptable accuracy and inference speed
simultaneously. In this paper, we start from an anchor-free detector called
TTFNet, modify the structure of TTFNet and introduce multiple existing tricks
to realize effective server and mobile solutions respectively. Since all
experiments in this paper are conducted based on PaddlePaddle, we call the
model as PAFNet(Paddle Anchor Free Network). For server side, PAFNet can
achieve a better balance between effectiveness (42.2% mAP) and efficiency
(67.15 FPS) on a single V100 GPU. For moblie side, PAFNet-lite can achieve a
better accuracy of (23.9% mAP) and 26.00 ms on Kirin 990 ARM CPU, outperforming
the existing state-of-the-art anchor-free detectors by significant margins.
Source code is at https://github.com/PaddlePaddle/PaddleDetection.
