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Object Detection Made Simpler by Eliminating Heuristic NMS

PyTorch Implementation for Our Paper: "Object Detection Made Simpler by Eliminating Heuristic NMS"

Requirements

Usage:

The code is being submitted to the company for open source review.

PSS for nms-free Object Detection:

End-to-End Training

Model Backbone lr sched mAP (COCO2017 val) link
FCOS R50 3x 42.0 model
ATSS R50 3x 42.8 model
FCOSpss R50 3x 42.3
ATSSpss R50 3x 42.6
VFNETpss R50 3x 44.0
FCOSpss R101 3x 44.1
ATSSpss R101 3x 44.2
VFNETpss R101 3x 45.7
FCOSpss X-101-DCN 2x 47.0
ATSSpss X-101-DCN 2x 47.3
VFNETpss X-101-DCN 2x 49.3
FCOSpss R2N-101-DCN 2x 48.2
ATSSpss R2N-101-DCN 2x 48.6
VFNETpss R2N-101-DCN 2x 50.0

NOTE: All models are trained with multi-scale training schedule of ‘[480, 800] $\times$ 1333’.

Two-Step Training

If we have a pretrained model, only finetuning the PSS head can save the training time.

Model Backbone MS Training lr sched mAP
pretrain model (w NMS)
mAP
finetuned PSS (w/o NMS)
GFocalV2pss R50 Yes 12 43.9 43.3
GFocalV2pss X-101-32x4d-DCN Yes 12 48.8 48.2
GFocalV2pss R2N-101-DCN Yes 12 49.9 49.2

PSS for nms-free Instance Segmentation

End-to-End Training

Model Backbone lr sched bbox mAP segm mAP link
CondInst R50 3x 41.9 37.5
CondInstpss R50 3x 41.2 36.7
CondInst + sem R50 3x 42.6 38.2
CondInstpss + sem R50 3x 42.3 37.7
CondInst R101 3x 43.3 38.6
CondInstpss R101 3x 43.1 38.2
CondInst + sem R101 3x 44.6 39.8
CondInstpss + sem R101 3x 44.1 39.3

NOTE: All models are trained with multi-scale training schedule of ‘[640, 800] $\times$ 1333’.

Citation

If you use the package in your research, please cite our paper:

@misc{zhou2021object,
      title={Object Detection Made Simpler by Eliminating Heuristic NMS}, 
      author={Qiang Zhou and Chaohui Yu and Chunhua Shen and Zhibin Wang and Hao Li},
      year={2021},
      eprint={2101.11782},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

PS

团队长期招聘中,社招/实习/校招,我们都要 ! 欢迎投递简历,邮箱: zhouqiang@zju.edu.cn

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