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SOLO: Segmenting Objects by Locations\n\nThis project hosts the code for implementing the SOLO algorithms for instance segmentation.\n\n\u003e [**SOLO: Segmenting Objects by Locations**](https://arxiv.org/abs/1912.04488),            \n\u003e Xinlong Wang, Tao Kong, Chunhua Shen, Yuning Jiang, Lei Li    \n\u003e In: Proc. European Conference on Computer Vision (ECCV), 2020  \n\u003e *arXiv preprint ([arXiv 1912.04488](https://arxiv.org/abs/1912.04488))*   \n\n\n\u003e [**SOLOv2: Dynamic and Fast Instance Segmentation**](https://arxiv.org/abs/2003.10152),            \n\u003e Xinlong Wang, Rufeng Zhang, Tao Kong, Lei Li, Chunhua Shen     \n\u003e In: Proc. Advances in Neural Information Processing Systems (NeurIPS), 2020  \n\u003e *arXiv preprint ([arXiv 2003.10152](https://arxiv.org/abs/2003.10152))*  \n\n\n![highlights](highlights.png)\n\n## Highlights\n- **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.\n- **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.\n- **High-quality mask prediction:** SOLOv2 is able to predict fine and detailed masks, especially at object boundaries.\n- **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.\n\n## Updates\n   - SOLOv2 implemented on detectron2 is released at [adet](https://github.com/aim-uofa/AdelaiDet/blob/master/configs/SOLOv2/README.md). (07/12/20)\n   - Training speeds up (~1.7x faster) for all models. (03/12/20)\n   - SOLOv2 is available. Code and trained models of SOLOv2 are released. (08/07/2020)\n   - Light-weight models and R101-based models are available. (31/03/2020) \n   - SOLOv1 is available. Code and trained models of SOLO and Decoupled SOLO are released. (28/03/2020)\n\n\n## Installation\nThis 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.\n\n## Models\nFor your convenience, we provide the following trained models on COCO (more models are coming soon).\nIf you need the models in [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) framework, please refer to [paddlepaddle/README.md](./paddlepaddle/README.MD).\n\n\nModel | Multi-scale training | Testing time / im | AP (minival) | Link\n--- |:---:|:---:|:---:|:---:\nSOLO_R50_1x | No | 77ms | 32.9 | [download](https://huggingface.co/xinlongwang/SOLO/resolve/main/SOLO_R50_1x.pth?download=true)\nSOLO_R50_3x | Yes | 77ms |  35.8 | [download](https://huggingface.co/xinlongwang/SOLO/resolve/main/SOLO_R50_3x.pth?download=true)\nSOLO_R101_3x | Yes | 86ms |  37.1 | [download](https://huggingface.co/xinlongwang/SOLO/resolve/main/SOLO_R101_3x.pth?download=true)\nDecoupled_SOLO_R50_1x | No | 85ms | 33.9 | [download](https://huggingface.co/xinlongwang/SOLO/resolve/main/DECOUPLED_SOLO_R50_1x.pth?download=true)\nDecoupled_SOLO_R50_3x | Yes | 85ms | 36.4 | [download](https://huggingface.co/xinlongwang/SOLO/resolve/main/DECOUPLED_SOLO_R50_3x.pth?download=true)\nDecoupled_SOLO_R101_3x | Yes | 92ms | 37.9 | [download](https://huggingface.co/xinlongwang/SOLO/resolve/main/DECOUPLED_SOLO_R101_3x.pth?download=true)\nSOLOv2_R50_1x | No | 54ms | 34.8 | [download](https://huggingface.co/xinlongwang/SOLO/resolve/main/SOLOv2_R50_1x.pth?download=true)\nSOLOv2_R50_3x | Yes | 54ms | 37.5 | [download](https://huggingface.co/xinlongwang/SOLO/resolve/main/SOLOv2_R50_3x.pth?download=true)\nSOLOv2_R101_3x | Yes | 66ms | 39.1 | [download](https://huggingface.co/xinlongwang/SOLO/resolve/main/SOLOv2_R101_3x.pth?download=true)\nSOLOv2_R101_DCN_3x | Yes | 97ms | 41.4 | [download](https://huggingface.co/xinlongwang/SOLO/resolve/main/SOLOv2_R101_DCN_3x.pth?download=true)\nSOLOv2_X101_DCN_3x | Yes | 169ms | 42.4 | [download](https://huggingface.co/xinlongwang/SOLO/resolve/main/SOLOv2_X101_DCN_3x.pth?download=true)\n\n**Light-weight models:**\n\nModel | Multi-scale training | Testing time / im | AP (minival) | Link\n--- |:---:|:---:|:---:|:---:\nDecoupled_SOLO_Light_R50_3x | Yes | 29ms | 33.0 | [download](https://huggingface.co/xinlongwang/SOLO/resolve/main/DECOUPLED_SOLO_LIGHT_R50_3x.pth?download=true)\nDecoupled_SOLO_Light_DCN_R50_3x | Yes | 36ms | 35.0 | [download](https://huggingface.co/xinlongwang/SOLO/resolve/main/DECOUPLED_SOLO_LIGHT_DCN_R50_3x.pth?download=true)\nSOLOv2_Light_448_R18_3x | Yes | 19ms | 29.6 | [download](https://huggingface.co/xinlongwang/SOLO/resolve/main/SOLOv2_LIGHT_448_R18_3x.pth?download=true)\nSOLOv2_Light_448_R34_3x | Yes | 20ms | 32.0 | [download](https://huggingface.co/xinlongwang/SOLO/resolve/main/SOLOv2_LIGHT_448_R34_3x.pth?download=true)\nSOLOv2_Light_448_R50_3x | Yes | 24ms | 33.7 | [download](https://huggingface.co/xinlongwang/SOLO/resolve/main/SOLOv2_LIGHT_448_R50_3x.pth?download=true)\nSOLOv2_Light_512_DCN_R50_3x | Yes | 34ms | 36.4 | [download](https://huggingface.co/xinlongwang/SOLO/resolve/main/SOLOv2_LIGHT_512_DCN_R50_3x.pth?download=true)\n\n*Disclaimer:*\n\n- Light-weight means light-weight backbone, head and smaller input size. Please refer to the corresponding config files for details.\n- This is a reimplementation and the numbers are slightly different from our original paper (within 0.3% in mask AP).\n\n\n## Usage\n\n### A quick demo\n\nOnce the installation is done, you can download the provided models and use [inference_demo.py](demo/inference_demo.py) to run a quick demo.\n\n### Train with multiple GPUs\n    ./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM}\n\n    Example: \n    ./tools/dist_train.sh configs/solo/solo_r50_fpn_8gpu_1x.py  8\n\n### Train with single GPU\n    python tools/train.py ${CONFIG_FILE}\n    \n    Example:\n    python tools/train.py configs/solo/solo_r50_fpn_8gpu_1x.py\n\n### Testing\n    # multi-gpu testing\n    ./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM}  --show --out  ${OUTPUT_FILE} --eval segm\n    \n    Example: \n    ./tools/dist_test.sh configs/solo/solo_r50_fpn_8gpu_1x.py SOLO_R50_1x.pth  8  --show --out results_solo.pkl --eval segm\n\n    # single-gpu testing\n    python tools/test_ins.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --show --out  ${OUTPUT_FILE} --eval segm\n    \n    Example: \n    python tools/test_ins.py configs/solo/solo_r50_fpn_8gpu_1x.py  SOLO_R50_1x.pth --show --out  results_solo.pkl --eval segm\n\n\n### Visualization\n\n    python tools/test_ins_vis.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --show --save_dir  ${SAVE_DIR}\n    \n    Example: \n    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\n\n## Contributing to the project\nAny pull requests or issues are welcome.\n\n## Citations\nPlease consider citing our papers in your publications if the project helps your research. BibTeX reference is as follows.\n```\n@inproceedings{wang2020solo,\n  title     =  {{SOLO}: Segmenting Objects by Locations},\n  author    =  {Wang, Xinlong and Kong, Tao and Shen, Chunhua and Jiang, Yuning and Li, Lei},\n  booktitle =  {Proc. Eur. Conf. Computer Vision (ECCV)},\n  year      =  {2020}\n}\n\n```\n\n```\n@article{wang2020solov2,\n  title={SOLOv2: Dynamic and Fast Instance Segmentation},\n  author={Wang, Xinlong and Zhang, Rufeng and  Kong, Tao and Li, Lei and Shen, Chunhua},\n  journal={Proc. Advances in Neural Information Processing Systems (NeurIPS)},\n  year={2020}\n}\n```\n\n## License\n\nFor 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](https://www.xloong.wang/) and  [Chunhua Shen](https://cs.adelaide.edu.au/~chhshen/).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwxinlong%2Fsolo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwxinlong%2Fsolo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwxinlong%2Fsolo/lists"}