  In computer vision, object detection is one of most important tasks, which
underpins a few instance-level recognition tasks and many downstream
applications. Recently one-stage methods have gained much attention over
two-stage approaches due to their simpler design and competitive performance.
Here we propose a fully convolutional one-stage object detector (FCOS) to solve
object detection in a per-pixel prediction fashion, analogue to other dense
prediction problems such as semantic segmentation. Almost all state-of-the-art
object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on
pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box
free, as well as proposal free. By eliminating the pre-defined set of anchor
boxes, FCOS completely avoids the complicated computation related to anchor
boxes such as calculating the intersection over union (IoU) scores during
training. More importantly, we also avoid all hyper-parameters related to
anchor boxes, which are often sensitive to the final detection performance.
With the only post-processing non-maximum suppression (NMS), we demonstrate a
much simpler and flexible detection framework achieving improved detection
accuracy. We hope that the proposed FCOS framework can serve as a simple and
strong alternative for many other instance-level tasks. Code and pre-trained
models are available at: https://git.io/AdelaiDet
