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"author": "Shingo Kitagawa",
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"description": "chainer-fcis - FCIS\n===================\n[](https://pypi.python.org/pypi/fcis)\n\n\n\n\nThis is [Chainer](https://github.com/chainer/chainer) implementation of [Fully Convolutional Instance-aware Semantic Segmentation](https://arxiv.org/abs/1611.07709).\n\nOriginal Mxnet repository is [msracver/FCIS](https://github.com/msracver/FCIS).\n\nRequirement\n-----------\n\n- [CuPy](https://github.com/cupy/cupy)\n- [Chainer](https://github.com/chainer/chainer)\n- [ChainerCV](https://github.com/chainer/chainercv)\n- OpenCV2\n\nAdditional Requirement\n----------------------\n- For COCO Dataset class\n - [Cython](http://cython.org/)\n - [pycocotools](https://github.com/cocodataset/cocoapi)\n \n- For COCO Training\n - [OpenMPI](https://www.open-mpi.org/)\n - [nccl](https://developer.nvidia.com/nccl)\n - [ChainerMN](https://github.com/chainer/chainermn)\n\nNotification\n------------\n- Only GPU implementation, No CPU implementation yet.\n\nTODO\n----\n- VOC\n - [x] Reproduce original repo training accuracy\n - [ ] Refine evaluation code\n- COCO\n - [ ] Reproduce original repo training accuracy\n - [ ] Refine evaluation code\n\nInstallation\n------------\n\nWe recommend to use [Anacoda](https://anaconda.org/).\n\n```bash\n# Requirement installation\nconda create -n fcis python=2.7\nconda install -c menpo opencv\npip install cupy\n\n# Installation\npip install fcis\n```\n\nInference\n---------\n```bash\ncd examples/coco/\npython demo.py\n```\n\nAbove is our implementation output, and below is original.\n\n![](\"static/output.png\")
\n![](\"static/original_output.png\")
\n\nTraining\n--------\n\n```bash\ncd examples/voc/\npython train.py\n```\n\nLICENSE\n-------\n[MIT LICENSE](LICENSE)\n\n\nPowered by [DL HACKS](http://deeplearning.jp/hacks/)",
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