SOLO and SOLOv2 for instance segmentation, ECCV 2020 & NeurIPS 2020.

SOLO: Segmenting Objects by Locations

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)

More code and models will be released soon. Stay tuned.

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

• 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).

Light-weight models:

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).

Usage

A quick demo

Once the installation is done, you can download the provided models and use inference_demo.py to run a quick demo.

Train with multiple GPUs

./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM}

Example: 
./tools/dist_train.sh configs/solo/solo_r50_fpn_8gpu_1x.py  8

Train with single GPU

python tools/train.py ${CONFIG_FILE}

Example:
python tools/train.py configs/solo/solo_r50_fpn_8gpu_1x.py

Testing

# 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

Visualization

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

Contributing to the project

Any pull requests or issues are welcome.

Citations

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}
}

License

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.