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}
}