本项目nonzero算子Mindspore暂时无法支持,以及SOLOv2动态学习卷积核的思想暂时与Mindspore的静态图哲学理念暂不兼容。
head,mask_head,backbone,fpn,dateset已经完成。
This project hosts the code for implementing the SOLO algorithms for instance segmentation.
SOLO: Segmenting Objects by Locations,
Xinlong Wang, Tao Kong, Chunhua Shen, Yuning Jiang, Lei Li
In: Proc. European Conference on Computer Vision (ECCV), 2020
arXiv preprint (arXiv 1912.04488)
SOLOv2: Dynamic and Fast Instance Segmentation,
Xinlong Wang, Rufeng Zhang, Tao Kong, Lei Li, Chunhua Shen
In: Proc. Advances in Neural Information Processing Systems (NeurIPS), 2020
arXiv preprint (arXiv 2003.10152)
Highlights
- Totally box-free: SOLO is totally box-free thus not being restricted by (anchor) box locations and scales, and naturally benefits from the inherent advantages of FCNs.
- Direct instance segmentation: Our method takes an image as input, directly outputs instance masks and corresponding class probabilities, in a fully convolutional, box-free and grouping-free paradigm.
- High-quality mask prediction: SOLOv2 is able to predict fine and detailed masks, especially at object boundaries.
- State-of-the-art performance: Our best single model based on ResNet-101 and deformable convolutions achieves 41.7% in AP on COCO test-dev (without multi-scale testing). A light-weight version of SOLOv2 executes at 31.3 FPS on a single V100 GPU and yields 37.1% AP.
Updates
- SOLOv2 implemented on detectron2 is released at adet. (07/12/20)
- Training speeds up (~1.7x faster) for all models. (03/12/20)
- SOLOv2 is available. Code and trained models of SOLOv2 are released. (08/07/2020)
- Light-weight models and R101-based models are available. (31/03/2020)
- SOLOv1 is available. Code and trained models of SOLO and Decoupled SOLO are released. (28/03/2020)
Installation
This implementation is based on mmdetection(v1.0.0). Please refer to INSTALL.md for installation and dataset preparation.
Models
For your convenience, we provide the following trained models on COCO (more models are coming soon).
If you need the models in PaddlePaddle framework, please refer to paddlepaddle/README.md.
Model |
Multi-scale training |
Testing time / im |
AP (minival) |
Link |
SOLO_R50_1x |
No |
77ms |
32.9 |
download |
SOLO_R50_3x |
Yes |
77ms |
35.8 |
download |
SOLO_R101_3x |
Yes |
86ms |
37.1 |
download |
Decoupled_SOLO_R50_1x |
No |
85ms |
33.9 |
download |
Decoupled_SOLO_R50_3x |
Yes |
85ms |
36.4 |
download |
Decoupled_SOLO_R101_3x |
Yes |
92ms |
37.9 |
download |
SOLOv2_R50_1x |
No |
54ms |
34.8 |
download |
SOLOv2_R50_3x |
Yes |
54ms |
37.5 |
download |
SOLOv2_R101_3x |
Yes |
66ms |
39.1 |
download |
SOLOv2_R101_DCN_3x |
Yes |
97ms |
41.4 |
download |
SOLOv2_X101_DCN_3x |
Yes |
169ms |
42.4 |
download |
Light-weight models:
Model |
Multi-scale training |
Testing time / im |
AP (minival) |
Link |
Decoupled_SOLO_Light_R50_3x |
Yes |
29ms |
33.0 |
download |
Decoupled_SOLO_Light_DCN_R50_3x |
Yes |
36ms |
35.0 |
download |
SOLOv2_Light_448_R18_3x |
Yes |
19ms |
29.6 |
download |
SOLOv2_Light_448_R34_3x |
Yes |
20ms |
32.0 |
download |
SOLOv2_Light_448_R50_3x |
Yes |
24ms |
33.7 |
download |
SOLOv2_Light_512_DCN_R50_3x |
Yes |
34ms |
36.4 |
download |
Disclaimer:
- Light-weight means light-weight backbone, head and smaller input size. Please refer to the corresponding config files for details.
- This is a reimplementation and the numbers are slightly different from our original paper (within 0.3% in mask AP).