Rotated FCOS
FCOS: Fully Convolutional One-Stage Object Detection
Abstract
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.
Results and Models
DOTA1.0
Notes:
MS
means multiple scale image split.
RR
means random rotation.
Rotated IoU Loss
need mmcv version 1.5.0 or above.
Separate Angle
means angle loss is calculated separately.
At this time bbox loss uses horizontal bbox loss such as IoULoss
, GIoULoss
.
- Tricks means setting
norm_on_bbox
, centerness_on_reg
, center_sampling
as True
.
- Inf time was tested on a single RTX3090.
Citation
@article{tian2019fcos,
title={FCOS: Fully Convolutional One-Stage Object Detection},
author={Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong},
journal={arXiv preprint arXiv:1904.01355},
year={2019}
}