ChainerCV is a collection of tools to train and run neural networks for computer vision tasks using Chainer.
You can find the documentation here.
Supported tasks:
- Object Detection (tutorial, Faster R-CNN, SSD)
- Semantic Segmentation (SegNet,)
- Image Classification (VGG,)
ChainerCV is developed under the following three guiding principles.
- Ease of Use -- Implementations of computer vision networks with a cohesive and simple interface.
- Reproducibility -- Training scripts that are perfect for being used as reference implementations.
- Compositionality -- Tools such as data loaders and evaluation scripts that have common API.
$ pip install -U numpy
$ pip install chainercv
The instruction on installation using Anaconda is here (recommended).
- Chainer and its dependencies
- Pillow
- Cython (Build requirements)
For additional features
- Matplotlib
- OpenCV
Environments under Python 2.7.12 and 3.6.0 are tested.
- The master branch will work on both the stable version (v2) and the development version (v3).
- For users using Chainer v1, please use version
0.4.11
, which can be installed bypip install chainercv==0.4.11
. This branch is unmaintained.
- Image
- The order of color channel is RGB.
- Shape is CHW (i.e.
(channel, height, width)
). - The range of values is
[0, 255]
. - Size is represented by row-column order (i.e.
(height, width)
).
- Bounding Boxes
- Shape is
(R, 4)
. - Coordinates are ordered as
(y_min, x_min, y_max, x_max)
. The order is the opposite of OpenCV.
- Shape is
- Semantic Segmentation Image
- Shape is
(height, weight)
. - The value is class id, which is in range
[0, n_class - 1]
.
- Shape is
These are the outputs of the detection models supported by ChainerCV.
If ChainerCV helps your research, please cite the paper for ACM Multimedia Open Source Software Competition. Here is a BibTeX entry:
@inproceedings{ChainerCV2017,
author = {Niitani, Yusuke and Ogawa, Toru and Saito, Shunta and Saito, Masaki},
title = {ChainerCV: a Library for Deep Learning in Computer Vision},
booktitle = {ACM Multimedia},
year = {2017},
}
The preprint can be found in arXiv: https://arxiv.org/abs/1708.08169