Metadata-Version: 1.0
Name: fcis
Version: 2.1.3
Summary: Chainer Implementation of FCIS
Home-page: https://github.com/knorth55/chainer-fcis
Author: Shingo Kitagawa
Author-email: shingogo.5511@gmail.com
License: MIT
Description-Content-Type: UNKNOWN
Description: chainer-fcis - FCIS
        ===================
        [![PyPI Version](https://img.shields.io/pypi/v/fcis.svg)](https://pypi.python.org/pypi/fcis)
        ![Build Status](https://travis-ci.org/knorth55/chainer-fcis.svg?branch=master)
        
        ![Example](static/coco_example.png)
        
        This is [Chainer](https://github.com/chainer/chainer) implementation of [Fully Convolutional Instance-aware Semantic Segmentation](https://arxiv.org/abs/1611.07709).
        
        Original Mxnet repository is [msracver/FCIS](https://github.com/msracver/FCIS).
        
        Requirement
        -----------
        
        - [CuPy](https://github.com/cupy/cupy)
        - [Chainer](https://github.com/chainer/chainer)
        - [ChainerCV](https://github.com/chainer/chainercv)
        - OpenCV2
        
        Additional Requirement
        ----------------------
        - For COCO Dataset class
          - [Cython](http://cython.org/)
          - [pycocotools](https://github.com/cocodataset/cocoapi)
          
        - For COCO Training
          - [OpenMPI](https://www.open-mpi.org/)
          - [nccl](https://developer.nvidia.com/nccl)
          - [ChainerMN](https://github.com/chainer/chainermn)
        
        Notification
        ------------
        - Only GPU implementation, No CPU implementation yet.
        
        TODO
        ----
        - VOC
          - [x] Reproduce original repo training accuracy
          - [ ] Refine evaluation code
        - COCO
          - [ ] Reproduce original repo training accuracy
          - [ ] Refine evaluation code
        
        Installation
        ------------
        
        We recommend to use [Anacoda](https://anaconda.org/).
        
        ```bash
        # Requirement installation
        conda create -n fcis python=2.7
        conda install -c menpo opencv
        pip install cupy
        
        # Installation
        pip install fcis
        ```
        
        Inference
        ---------
        ```bash
        cd examples/coco/
        python demo.py
        ```
        
        Above is our implementation output, and below is original.
        
        <img src="static/output.png" width="60%" >
        <img src="static/original_output.png" width="60%" >
        
        Training
        --------
        
        ```bash
        cd examples/voc/
        python train.py
        ```
        
        LICENSE
        -------
        [MIT LICENSE](LICENSE)
        
        
        Powered by [DL HACKS](http://deeplearning.jp/hacks/)
        
Keywords: machine-learning
Platform: UNKNOWN
