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res2net

README.md

Res2Net for object detection and instance segmentation

Introduction

[ALGORITHM]

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},
}

Results and Models

Faster R-CNN

Backbone Style Lr schd Mem (GB) Inf time (fps) box AP Config Download
R2-101-FPN pytorch 2x 7.4 - 43.0 config model | log

Mask R-CNN

Backbone Style Lr schd Mem (GB) Inf time (fps) box AP mask AP Config Download
R2-101-FPN pytorch 2x 7.9 - 43.6 38.7 config model | log

Cascade R-CNN

Backbone Style Lr schd Mem (GB) Inf time (fps) box AP Config Download
R2-101-FPN pytorch 20e 7.8 - 45.7 config model | log

Cascade Mask R-CNN

Backbone Style Lr schd Mem (GB) Inf time (fps) box AP mask AP Config Download
R2-101-FPN pytorch 20e 9.5 - 46.4 40.0 config model | log

Hybrid Task Cascade (HTC)

Backbone Style Lr schd Mem (GB) Inf time (fps) box AP mask AP Config Download
R2-101-FPN pytorch 20e - - 47.5 41.6 config model | log