you only look one-level feature

Qiang Chen, Yingming Wang, Tong Yang, Xiangyu Zhang, Jian Cheng, Jian Sun

Abstract:

This paper revisits feature pyramids networks (FPN) for
one-stage detectors and points out that the success of FPN
is due to its divide-and-conquer solution to the optimization problem in object detection rather than multi-scale feature fusion. From the perspective of optimization, we introduce an alternative way to address the problem instead
of adopting the complex feature pyramids - utilizing only
one-level feature for detection. Based on the simple and efficient solution, we present You Only Look One-level Feature (YOLOF). In our method, two key components, Dilated Encoder and Uniform Matching, are proposed and
bring considerable improvements. Extensive experiments
on the COCO benchmark prove the effectiveness of the proposed model. Our YOLOF achieves comparable results
with its feature pyramids counterpart RetinaNet while being 2.5× faster. Without transformer layers, YOLOF can
match the performance of DETR in a single-level feature
manner with 7× less training epochs. Code is available at
https://github.com/megvii-model/YOLOF.