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mmdetection

README.md

InstaBoost on mmdetection

Codes in this folder is an implementation of InstaBoost for mmdetection v0.6.0.

Installation, Training, Testing

Install mmdetection according to INSTALL.md. Train or test models according to ORIREADME.md.

Implementation

Users can simply implement InstaBoost on mmdetection framework by changing codes in mmdet/datasets/custom.py, after import InstaBoost here.

The reason for modifying these codes is get_new_data function need variable img as input. Thus, get_ann_info function need to be expand by deleting

ann = self.get_ann_info(idx)

and adding

        img_id = self.img_infos[idx]['id']
        ann_ids = self.coco.getAnnIds(imgIds=[img_id])
        ann_info = self.coco.loadAnns(ann_ids)

        aug_flag = np.random.choice([0,1],p=[0.5,0.5])
        if aug_flag:
            ann_info, img = get_new_data(ann_info, img, None, background=None)

        ann = self._parse_ann_info(ann_info, self.with_mask)

Configurations

4x configurations are available in InstaBoost_configs.

Results and models

For your conveinience of evaluation and comparison, we report the evaluation number on COCO val below. In our paper, the numbers are obtained from test-dev.

InstaBoost Network Backbone Lr schd box AP mask AP Download
× Mask R-CNN R-50-FPN 1x 37.3 34.2 -
√ Mask R-CNN R-50-FPN 4x 40.0 36.2 Baidu / Google
× Mask R-CNN R-101-FPN 1x 39.4 35.9 -
√ Mask R-CNN R-101-FPN 4x 42.1 37.8 Baidu / Google
× Mask R-CNN X-101-64x4d-FPN 1x 42.1 38.0 -
× Mask R-CNN X-101-64x4d-FPN 2x 42.0 37.7 -
√ Mask R-CNN X-101-64x4d-FPN 4x 44.5 39.5 Baidu / Google
× Cascade R-CNN R-101-FPN 1x 42.6 37.0 -
√ Cascade R-CNN R-101-FPN 4x 45.4 39.2 Baidu / Google
× Cascade R-CNN X-101-64x4d-FPN 1x 45.4 39.1 -
√ Cascade R-CNN X-101-64x4d-FPN 4x 47.2 40.4 Baidu / Google
× SSD VGG16-512 120e 29.3 - -
√ SSD VGG16-512 360e 30.3 - Baidu / Google

Citation

If you use this toolbox or benchmark in your research, please cite this project.

@article{Fang2019InstaBoost,
author = {Fang, Hao-Shu and Sun, Jianhua and Wang, Runzhong and Gou, Minghao and Li, Yong-Lu and Lu, Cewu},
title = {InstaBoost: Boosting Instance Segmentation Via Probability Map Guided Copy-Pasting},
journal={arXiv preprint arXiv:1908.07801},
year = {2019}
}

If you use this version of mmdetection, please also citing their original repo:

@article{mmdetection,
  title   = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark},
  author  = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and
             Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and
             Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and
             Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and
             Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong
             and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua},
  journal= {arXiv preprint arXiv:1906.07155},
  year={2019}
}