
# SOLO: Segmenting Objects by Locations

This project hosts the code for implementing the SOLO algorithms for instance segmentation.

> [**SOLO: Segmenting Objects by Locations**](https://arxiv.org/abs/1912.04488),            
> Xinlong Wang, Tao Kong, Chunhua Shen, Yuning Jiang, Lei Li        
> *arXiv preprint ([arXiv 1912.04488](https://arxiv.org/abs/1912.04488))*   


> [**SOLOv2: Dynamic, Faster and Stronger**](https://arxiv.org/abs/2003.10152),            
> Xinlong Wang, Rufeng Zhang, Tao Kong, Lei Li, Chunhua Shen        
> *arXiv preprint ([arXiv 2003.10152](https://arxiv.org/abs/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.
- **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
   - 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](https://github.com/open-mmlab/mmdetection)(v1.0.0). Please refer to [INSTALL.md](docs/INSTALL.md) for installation and dataset preparation.

## Models
For your convenience, we provide the following trained models on COCO (more models are coming soon).

Model | Multi-scale training | Testing time / im | AP (minival) | Link
--- |:---:|:---:|:---:|:---:
SOLO_R50_1x | No | 77ms | 32.9 | [download](https://cloudstor.aarnet.edu.au/plus/s/nTOgDldI4dvDrPs/download)
SOLO_R50_3x | Yes | 77ms |  35.8 | [download](https://cloudstor.aarnet.edu.au/plus/s/x4Fb4XQ0OmkBvaQ/download)
SOLO_R101_3x | Yes | 86ms |  37.1 | [download](https://cloudstor.aarnet.edu.au/plus/s/WxOFQzHhhKQGxDG/download)
Decoupled_SOLO_R50_1x | No | 85ms | 33.9 | [download](https://cloudstor.aarnet.edu.au/plus/s/RcQyLrZQeeS6JIy/download)
Decoupled_SOLO_R50_3x | Yes | 85ms | 36.4 | [download](https://cloudstor.aarnet.edu.au/plus/s/dXz11J672ax0Z1Q/download)
Decoupled_SOLO_R101_3x | Yes | 92ms | 37.9 | [download](https://cloudstor.aarnet.edu.au/plus/s/BRhKBimVmdFDI9o/download)

**Light-weight models:**

Model | Multi-scale training | Testing time / im | AP (minival) | Link
--- |:---:|:---:|:---:|:---:
DECOUPLED_SOLO_LIGHT_R50_3x | Yes | 29ms | 33.0 | [download](https://cloudstor.aarnet.edu.au/plus/s/d0zuZgCnAjeYvod/download)
DECOUPLED_SOLO_LIGHT_DCN_R50_3x | Yes | 36ms | 35.0 | [download](https://cloudstor.aarnet.edu.au/plus/s/QvWhOTmCA5pFj6E/download)

## Usage

### A quick demo

Once the installation is done, you can download the provided models and use [inference_demo.py](demo/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
    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_FPN_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_FPN_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.
```
@article{wang2019solo,
  title={SOLO: Segmenting Objects by Locations},
  author={Wang, Xinlong and Kong, Tao and Shen, Chunhua and Jiang, Yuning and Li, Lei},
  journal={arXiv preprint arXiv:1912.04488},
  year={2019}
}
```

```
@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}
}
```
