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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

Backbone mAP Angle Separate Angle Tricks lr schd Mem (GB) Inf Time (fps) Aug Batch Size Configs Download
ResNet50 (1024,1024,200) 70.70 le90 Y Y 1x 4.18 26.4 - 2 rotated_fcos_sep_angle_r50_fpn_1x_dota_le90 model | log
ResNet50 (1024,1024,200) 71.28 le90 N Y 1x 4.18 25.9 - 2 rotated_fcos_r50_fpn_1x_dota_le90 model | log
ResNet50 (1024,1024,200) 71.76 le90 Y Y 1x 4.23 25.7 - 2 rotated_fcos_csl_gaussian_r50_fpn_1x_dota_le90 model | log
ResNet50 (1024,1024,200) 71.89 le90 N Y 1x 4.18 26.2 - 2 rotated_fcos_kld_r50_fpn_1x_dota_le90 model | log

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}
}