surveillance_monitoring/dependence/PaddleDetection/static/configs/solov2

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:

• Performance: Light-R50-VD-DCN-FPN model reached 38.6 FPS on single Tesla V100, and mask ap on the COCO-val dataset reached 38.8, which increased inference speed by 24%, mAP increased by 2.4 percentage points.
• 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	Mobilenetv3-FPN	True	3x	30.0	50	V100	model	config
SOLOv2	R50-FPN	False	1x	35.6	21.9	V100	model	config
SOLOv2	R50-FPN	True	3x	37.9	21.9	V100	model	config
SOLOv2	R101-VD-FPN	True	3x	42.6	12.1	V100	model	config

Enhanced model

Backbone	Input size	Lr schd	V100 FP32(FPS)	Mask APval	Download	Configs
Light-R50-VD-DCN-FPN	512	3x	38.6	38.8	model	config

Notes:

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

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}
}