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 AP^val	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

Notes:

SOLOv2 is trained on COCO train2017 dataset and evaluated on val2017 results of mAP(IoU=0.5:0.95).
SOLOv2 training performace is dependented on Paddle develop branch, performance reproduction shoule based on Paddle daily version or Paddle 2.0.1(will be published on 2021.03), performace will loss slightly is training base on Paddle 2.0.0

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}
}

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SOLOv2 for instance segmentation

Introduction

Model Zoo

Citations

2.7 KiB

Raw Blame History