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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 AP<sup>val</sup> |  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](https://paddledet.bj.bcebos.com/models/solov2_r50_fpn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/solov2/solov2_r50_fpn_1x_coco.yml) |
| SOLOv2 | R50-FPN                 |  True                |   3x    |     38.0         |   21.9    | V100 |  [model](https://paddledet.bj.bcebos.com/models/solov2_r50_fpn_3x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/solov2/solov2_r50_fpn_3x_coco.yml) |
| SOLOv2 | R101vd-FPN                 |  True                |   3x    |     42.7         |   12.1    | V100 |  [model](https://paddledet.bj.bcebos.com/models/solov2_r101_vd_fpn_3x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/solov2/solov2_r101_vd_fpn_3x_coco.yml) |

**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 AP<sup>val</sup> |         Download                  | Configs |
| :---------------------: | :-------------------: | :-----: | :------------: | :-----: | :---------: | :------------------------: |
| Light-R50-VD-DCN-FPN          |  512     |   3x    |     38.6          |  39.0   | [model](https://paddledet.bj.bcebos.com/models/solov2_r50_enhance_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/solov2/solov2_r50_enhance_coco.yml) |

**Optimizing method of enhanced model:**
- Better backbone network: ResNet50vd-DCN
- A better pre-training model for knowledge distillation
- [Exponential Moving Average](https://www.investopedia.com/terms/e/ema.asp)
- 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}
}
```

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提交于 2022-04-02 17:53 . update link for release/2.4. (#5489)

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
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 AP^val	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}
}

Python

1

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git@gitee.com:paddlepaddle/PaddleDetection.git

paddlepaddle

PaddleDetection

PaddleDetection

release/2.4