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_1xon Tesla v100 with 8 GPU is only 10 hours.
| 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 |
Notes:
- SOLOv2 is trained on COCO train2017 dataset and evaluated on val2017 results of
mAP(IoU=0.5:0.95).
@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}
}