Chainer Implementation of FCIS
Project description
chainer-fcis - 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
------------
```bash
# Requirement installation
# I recommend to use anacoda.
conda create -n fcis python=2.7
conda install -c menpo opencv
pip install cupy
# Installation
git clone https://github.com/knorth55/chainer-fcis.git
cd chainer-fcis
pip install -e .
```
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/)
===================
![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
------------
```bash
# Requirement installation
# I recommend to use anacoda.
conda create -n fcis python=2.7
conda install -c menpo opencv
pip install cupy
# Installation
git clone https://github.com/knorth55/chainer-fcis.git
cd chainer-fcis
pip install -e .
```
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/)
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
fcis-2.1.0.tar.gz
(1.5 MB
view hashes)