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CoupleNet

CoupleNet: Coupling Global Structure with Local Parts for Object Detection

The Code is modified from py-R-FCN, please follow the procedure in it to prepare the training data and testing data. Using the default hyperparameters and iterations, you can achieve a mAP around 81.7%.<br>

Main results

	training data	test data	mAP@0.5	time/img(ms)
CoupleNet, ResNet-101<sup>**</sup>	VOC 07+12	VOC 07 test	81.7%	102
CoupleNet, ResNet-101	VOC 07+12	VOC 07 test	82.1%	122
CoupleNet, ResNet-101	VOC 07++12	VOC 12 test	80.4%	122

**: without adding context.

	training data	test data	mAP@[0.5:0.95]	time/img(ms)
CoupleNet, ResNet-101	COCO 2014 trainval	COCO test dev	34.4%	122

VOC 0712 model (trained on VOC 07+12, mAP 81.7%)

Citing CoupleNet

If you find CoupleNet useful in your research, please consider citing:

@article{zhu2017couplenet,
    title={CoupleNet: Coupling Global Structure with Local Parts for Object Detection},
    author={Zhu, Yousong and Zhao, Chaoyang and Wang, Jinqiao and Zhao, Xu and Wu, Yi and Lu, Hanqing},
    journal={arXiv preprint arXiv:1708.02863},
    year={2017}
}