# 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](https://paddledet.bj.bcebos.com/models/solov2_r50_fpn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/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/develop/configs/solov2/solov2_r50_fpn_3x_coco.yml) | **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} } ```