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CBNet: A Novel Composite Backbone Network Architecture for Object Detection

by Yudong Liu, Tingting Liang, Yongtao Wang.

Introduction

We have released our code at https://github.com/PKUbahuangliuhe/CBNet implemented by caffe2. Since Detectron code will not be maintained, we release our implementation based on mmdetection

Please follow mmdetection on how to install the environment.

You need to convert the original backbone to cbnet version with python convert_db.py or python convert_tb.py.

Our CBNetv2 will be released soon.

Contact us with [email protected], [email protected], [email protected].

The project is only free for academic research purposes, but needs authorization for commerce. For commerce permission, please contact [email protected].

Citation

If you use our code/model/data, please cite our paper: https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuY.1833.pdf

Detection results on COCO val2017

Baseline Backbone Input size box AP
FPN ResNext-101-32x4d 1333x800 40.1
FPN Dual-ResNeXt-101-32x4d 1333x800 41.5
FPN Triple-ResNeXt-101-32x4d 1333x800 42.0
FPN Dual-ResNeXt-101-32x4d(CBNetv2) 1333x800 43.2
Cascade R-CNN(with DCN and multi-scale training) Dual-ResNeXt-101-32x4d(CBNetv2) 1333x800 51.2(51.6 on 2017test-dev)

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