  We propose a fully convolutional one-stage object detector (FCOS) to solve
object detection in a per-pixel prediction fashion, analogue to 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 predefined set of anchor boxes, FCOS completely avoids the
complicated computation related to anchor boxes such as calculating overlapping
during training. More importantly, we also avoid all hyper-parameters related
to anchor boxes, which are often very sensitive to the final detection
performance. With the only post-processing non-maximum suppression (NMS), FCOS
with ResNeXt-64x4d-101 achieves 44.7% in AP with single-model and single-scale
testing, surpassing previous one-stage detectors with the advantage of being
much simpler. For the first time, 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 is available at:Code is available at:
https://tinyurl.com/FCOSv1
