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res2net

README.md

Res2Net for object detection and instance segmentation

Introduction

We propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer.

Backbone Params. GFLOPs top-1 err. top-5 err.
ResNet-101 44.6 M 7.8 22.63 6.44
ResNeXt-101-64x4d 83.5M 15.5 20.40 -
HRNetV2p-W48 77.5M 16.1 20.70 5.50
Res2Net-101 45.2M 8.3 18.77 4.64

Compared with other backbone networks, Res2Net requires fewer parameters and FLOPs.

Note:

  • GFLOPs for classification are calculated with image size (224x224).
@article{gao2019res2net,
  title={Res2Net: A New Multi-scale Backbone Architecture},
  author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip},
  journal={IEEE TPAMI},
  year={2020},
  doi={10.1109/TPAMI.2019.2938758},
}

Detection Results

Faster R-CNN

Backbone Params. GFLOPs box AP
R-101-FPN 60.52M 283.14 39.4
X-101-64x4d-FPN 99.25M 440.36 41.3
HRNetV2p-W48 83.36M 459.66 41.5
Res2Net-101 61.18M 293.68 42.3

Mask R-CNN

Backbone Params. GFLOPs box AP mask AP
R-101-FPN 63.17M 351.65 40.3 36.5
X-101-64x4d-FPN 101.9M 508.87 42.0 37.7
HRNetV2p-W48 86.01M 528.17 42.9 38.3
Res2Net-101 63.83M 362.18 43.3 38.6

Cascade R-CNN

Backbone Params. GFLOPs box AP
R-101-FPN 88.16M 310.78 42.5
X-101-64x4d-FPN 126.89M 468.00 44.7
HRNetV2p-W48 111.00M 487.30 44.6
Res2Net-101 88.82M 321.32 45.5

Cascade Mask R-CNN

Backbone Params. GFLOPs box AP mask AP
R-101-FPN 96.09M 516.30 43.3 37.6
X-101-64x4d-FPN 134.82M 673.52 45.7 39.4
HRNetV2p-W48 118.93M 692.82 46.0 39.5
Res2Net-101 96.75M 526.84 46.1 39.4

Hybrid Task Cascade (HTC)

Backbone Params. GFLOPs box AP mask AP
R-101-FPN 99.03M 563.76 44.9 39.4
X-101-64x4d-FPN 137.75M 720.98 46.9 40.8
HRNetV2p-W48 121.87M 740.28 47.0 41.0
Res2Net-101 99.69M 574.30 47.5 41.3

Note:

  • GFLOPs are calculated with image size (1280, 800).
  • All detection methods in this page use pytorch style. Lr schd is 2x for Faster R-CNN and Mask R-CNN, and 20e for others.
  • Res2Net ImageNet pretrained models are in Res2Net-PretrainedModels.
  • More applications of Res2Net are in Res2Net-Github.