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SOLOv2 for instance segmentation

Introduction

SOLOv2 (Segmenting Objects by Locations) is a fast instance segmentation framework with strong performance. We reproduced the model of the paper, and improved and optimized the accuracy and speed of the SOLOv2.

Highlights:

  • Training Time: The training time of the model of solov2_r50_fpn_1x on Tesla v100 with 8 GPU is only 10 hours.

Model Zoo

Detector Backbone Multi-scale training Lr schd Mask APval V100 FP32(FPS) GPU Download Configs
YOLACT++ R50-FPN False 80w iter 34.1 (test-dev) 33.5 Xp - -
CenterMask R50-FPN True 2x 36.4 13.9 Xp - -
CenterMask V2-99-FPN True 3x 40.2 8.9 Xp - -
PolarMask R50-FPN True 2x 30.5 9.4 V100 - -
BlendMask R50-FPN True 3x 37.8 13.5 V100 - -
SOLOv2 (Paper) R50-FPN False 1x 34.8 18.5 V100 - -
SOLOv2 (Paper) X101-DCN-FPN True 3x 42.4 5.9 V100 - -
SOLOv2 R50-FPN False 1x 35.5 21.9 V100 model config
SOLOv2 R50-FPN True 3x 38.0 21.9 V100 model config
SOLOv2 R101vd-FPN True 3x 42.7 12.1 V100 model config

Notes:

  • SOLOv2 is trained on COCO train2017 dataset and evaluated on val2017 results of mAP(IoU=0.5:0.95).

Enhanced model

Backbone Input size Lr schd V100 FP32(FPS) Mask APval Download Configs
Light-R50-VD-DCN-FPN 512 3x 38.6 39.0 model config

Optimizing method of enhanced model:

  • Better backbone network: ResNet50vd-DCN
  • A better pre-training model for knowledge distillation
  • Exponential Moving Average
  • Synchronized Batch Normalization
  • Multi-scale training
  • More data augmentation methods
  • DropBlock

Citations

@article{wang2020solov2,
  title={SOLOv2: Dynamic, Faster and Stronger},
  author={Wang, Xinlong and Zhang, Rufeng and  Kong, Tao and Li, Lei and Shen, Chunhua},
  journal={arXiv preprint arXiv:2003.10152},
  year={2020}
}