zcdliuwei / TSD

1st place models in Google OpenImage Detection Challenge 2019

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News: We reimplement the TSD algorithm based on the MMDetection framework.


TSD (https://arxiv.org/abs/2003.07540)

1st place solutions for openimage 2019 (https://arxiv.org/abs/2003.07557)


The installation of MMDetection can be found from the official github(https://github.com/open-mmlab/mmdetection)

TSD is a plugin detector head which is friendly to any anchor-based two stage detectors (Faster RCNN, Mask RCNN and so on).



V1.0: We firstly reimplement the experiments based on Faster RCNN with Resnet families.

The SharedFCBBoxHead is used as the sibling head.

The corresponding configuration can be found in (faster_rcnn_r50_fpn_TSD_1x.py, faster_rcnn_r101_fpn_TSD_1x.py, faster_rcnn_r152_fpn_TSD_1x.py)


  1. LR can be set to base_lr*total_batch (base_lr=0.00125, 0.04 = 0.00125*32 in our experiments.)
  2. An external epoch can be used to perform warmup. (base_lr will be incresed to LR in the first epoch)


Reimplemented methods and backbones are shown in the below table. It's based on the Faster RCNN with FPN. More backbones and experiments are underway.

Backbone TSD AP AP_0.5 AP_0.75 AP_s AP_m AP_l Download
ResNet50 36.2 58.1 39.0 21.8 39.9 46.1
ResNet50 40.9 61.9 44.4 24.2 44.4 54.0 model
ResNet101 38.9 60.6 42.4 22.3 43.6 50.6
ResNet101 42.3 63.1 45.9 25.1 46.3 56.5 model
ResNet152 40.5 62.1 44.5 24.6 45.0 51.8
ResNet152 43.7 64.5 47.6 26.1 48.0 57.5 model


We will continue to update the pretrained models of some heavy backbones.


Please refer to MMdetection for installation and dataset preparation.

Get Started

./tools/slurm_train.sh dev TSD configs/faster_rcnn_r152_fpn_TSD_1x.py exp/TSD_r152/ 16


We sinerely appreciate the support of MMDetection for object detection algorithms.


If the TSD helps your research, please cite the follow papers.

  title={Revisiting the Sibling Head in Object Detector},
  author={Song, Guanglu and Liu, Yu and Wang, Xiaogang},
  journal={arXiv preprint arXiv:2003.07540},
  title={1st Place Solutions for OpenImage2019--Object Detection and Instance Segmentation},
  author={Liu, Yu and Song, Guanglu and Zang, Yuhang and Gao, Yan and Xie, Enze and Yan, Junjie and Loy, Chen Change and Wang, Xiaogang},
  journal={arXiv preprint arXiv:2003.07557},


If you have any questions, please contact (songguanglu@sensetime.com).

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1st place models in Google OpenImage Detection Challenge 2019

License:Apache License 2.0


Language:Python 86.6%Language:Cuda 7.9%Language:C++ 5.4%Language:Shell 0.1%Language:Dockerfile 0.0%