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)
- 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.
- 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)
This implementation is based on mmdetection(v1.0.0). Please refer to INSTALL.md for installation and dataset preparation.
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).
Once the installation is done, you can download the provided models and use inference_demo.py to run a quick demo.
./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM}
Example:
./tools/dist_train.sh configs/solo/solo_r50_fpn_8gpu_1x.py 8
python tools/train.py ${CONFIG_FILE}
Example:
python tools/train.py configs/solo/solo_r50_fpn_8gpu_1x.py
# multi-gpu testing
./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} --show --out ${OUTPUT_FILE} --eval segm
Example:
./tools/dist_test.sh configs/solo/solo_r50_fpn_8gpu_1x.py SOLO_R50_1x.pth 8 --show --out results_solo.pkl --eval segm
# single-gpu testing
python tools/test_ins.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --show --out ${OUTPUT_FILE} --eval segm
Example:
python tools/test_ins.py configs/solo/solo_r50_fpn_8gpu_1x.py SOLO_R50_1x.pth --show --out results_solo.pkl --eval segm
python tools/test_ins_vis.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --show --save_dir ${SAVE_DIR}
Example:
python tools/test_ins_vis.py configs/solo/solo_r50_fpn_8gpu_1x.py SOLO_R50_1x.pth --show --save_dir work_dirs/vis_solo
Any pull requests or issues are welcome.
Please consider citing our papers in your publications if the project helps your research. BibTeX reference is as follows.
@inproceedings{wang2020solo,
title = {{SOLO}: Segmenting Objects by Locations},
author = {Wang, Xinlong and Kong, Tao and Shen, Chunhua and Jiang, Yuning and Li, Lei},
booktitle = {Proc. Eur. Conf. Computer Vision (ECCV)},
year = {2020}
}
@article{wang2020solov2,
title={SOLOv2: Dynamic and Fast Instance Segmentation},
author={Wang, Xinlong and Zhang, Rufeng and Kong, Tao and Li, Lei and Shen, Chunhua},
journal={Proc. Advances in Neural Information Processing Systems (NeurIPS)},
year={2020}
}
For academic use, this project is licensed under the 2-clause BSD License - see the LICENSE file for details. For commercial use, please contact Xinlong Wang and Chunhua Shen.