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https://github.com/chainer/chainercv
ChainerCV: a Library for Deep Learning in Computer Vision
https://github.com/chainer/chainercv
chainer chainercv computer-vision cupy deep-learning neural-network python
Last synced: about 1 month ago
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ChainerCV: a Library for Deep Learning in Computer Vision
- Host: GitHub
- URL: https://github.com/chainer/chainercv
- Owner: chainer
- License: mit
- Archived: true
- Created: 2017-02-13T04:15:10.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2021-07-01T16:54:50.000Z (over 3 years ago)
- Last Synced: 2024-09-22T00:02:18.718Z (about 2 months ago)
- Topics: chainer, chainercv, computer-vision, cupy, deep-learning, neural-network, python
- Language: Python
- Homepage:
- Size: 5.6 MB
- Stars: 1,482
- Watchers: 73
- Forks: 305
- Open Issues: 58
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
[](http://chainercv.readthedocs.io/en/stable/)
[](https://pypi.python.org/pypi/chainercv)
[](https://github.com/chainer/chainercv/blob/master/LICENSE)
[](https://travis-ci.org/chainer/chainercv)
[](http://chainercv.readthedocs.io/en/latest/?badge=latest)
# ChainerCV: a Library for Deep Learning in Computer Vision
ChainerCV is a collection of tools to train and run neural networks for computer vision tasks using [Chainer](https://github.com/chainer/chainer).
You can find the documentation [here](http://chainercv.readthedocs.io/en/stable/).
Supported tasks:
+ Image Classification ([ResNet](examples/resnet), [SENet](examples/senet), [VGG](examples/vgg))
+ Object Detection ([tutorial](http://chainercv.readthedocs.io/en/latest/tutorial/detection.html), [Faster R-CNN](examples/faster_rcnn), [FPN](examples/fpn), [SSD](examples/ssd), [YOLO](examples/yolo), [Light-Head R-CNN](examples/light_head_rcnn))
+ Semantic Segmentation ([SegNet](examples/segnet), [PSPNet](examples/pspnet), [DeepLab v3+](examples/deeplab))
+ Instance Segmentation ([FCIS](examples/fcis), [Mask R-CNN](examples/fpn))
# Guiding Principles
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.
# Installation
```bash
$ pip install -U numpy
$ pip install chainercv
```
The instruction on installation using Anaconda is [here](http://chainercv.readthedocs.io/en/stable/#install-guide) (recommended).
### Requirements
+ [Chainer](https://github.com/chainer/chainer) and its dependencies
+ Pillow
+ Cython (Build requirements)
For additional features
+ Matplotlib
+ OpenCV
+ SciPy
+ mpi4py
+ [pycocotools](https://github.com/cocodataset/cocoapi/tree/master/PythonAPI/pycocotools)
Environments under Python 2.7.12 and 3.6.0 are tested.
+ The master branch is designed to work on Chainer v6 (the stable version) and v7 (the development version).
+ The following branches are kept for the previous version of Chainer. Note that these branches are unmaintained.
+ `0.4.11` (for Chainer v1). It can be installed by `pip install chainercv==0.4.11`.
+ `0.7` (for Chainer v2). It can be installed by `pip install chainercv==0.7`.
+ `0.8` (for Chainer v3). It can be installed by `pip install chainercv==0.8`.
+ `0.10` (for Chainer v4). It can be installed by `pip install chainercv==0.10`.
+ `0.12` (for Chainer v5). It can be installed by `pip install chainercv==0.12`.
+ `0.13` (for Chainer v6). It can be installed by `pip install chainercv==0.13`.
# Data Conventions
+ 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.
+ Semantic Segmentation Image
+ Shape is `(height, width)`.
+ The value is class id, which is in range `[0, n_class - 1]`.
# Sample Visualization
These are the outputs of the detection models supported by ChainerCV.
# Citation
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