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InstaBoost for MMDetection

Configs in this directory is the implementation for ICCV2019 paper "InstaBoost: Boosting Instance Segmentation Via Probability Map Guided Copy-Pasting" and provided by the authors of the paper. InstaBoost is a data augmentation method for object detection and instance segmentation. The paper has been released on arXiv.

@inproceedings{fang2019instaboost,
  title={Instaboost: Boosting instance segmentation via probability map guided copy-pasting},
  author={Fang, Hao-Shu and Sun, Jianhua and Wang, Runzhong and Gou, Minghao and Li, Yong-Lu and Lu, Cewu},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={682--691},
  year={2019}
}

Usage

Requirements

You need to install instaboostfast before using it.

pip install instaboostfast

The code and more details can be found here.

Integration with MMDetection

InstaBoost have been already integrated in the data pipeline, thus all you need is to add or change InstaBoost configurations after LoadImageFromFile. We have provided examples like this. You can refer to InstaBoostConfig for more details.

Results and Models

  • All models were trained on coco_2017_train and tested on coco_2017_val for conveinience of evaluation and comparison. In the paper, the results are obtained from test-dev.
  • To balance accuracy and training time when using InstaBoost, models released in this page are all trained for 48 Epochs. Other training and testing configs strictly follow the original framework.
  • The results and models are provided by the authors (many thanks).
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