{"id":13528966,"url":"https://github.com/chainer/chainercv","last_synced_at":"2025-09-28T21:31:33.063Z","repository":{"id":48993439,"uuid":"81784084","full_name":"chainer/chainercv","owner":"chainer","description":"ChainerCV: a Library for Deep Learning in Computer 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the Docs](https://readthedocs.org/projects/chainercv/badge/?version=latest)](http://chainercv.readthedocs.io/en/latest/?badge=latest)\n\n# ChainerCV: a Library for Deep Learning in Computer Vision\n\nChainerCV is a collection of tools to train and run neural networks for computer vision tasks using [Chainer](https://github.com/chainer/chainer).\n\nYou can find the documentation [here](http://chainercv.readthedocs.io/en/stable/).\n\nSupported tasks:\n\n+ Image Classification ([ResNet](examples/resnet), [SENet](examples/senet), [VGG](examples/vgg))\n+ 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))\n+ Semantic Segmentation ([SegNet](examples/segnet), [PSPNet](examples/pspnet), [DeepLab v3+](examples/deeplab))\n+ Instance Segmentation ([FCIS](examples/fcis), [Mask R-CNN](examples/fpn))\n\n# Guiding Principles\nChainerCV is developed under the following three guiding principles.\n\n+ **Ease of Use** -- Implementations of computer vision networks with a cohesive and simple interface.\n+ **Reproducibility** -- Training scripts that are perfect for being used as reference implementations.\n+ **Compositionality** -- Tools such as data loaders and evaluation scripts that have common API.\n\n# Installation\n\n```bash\n$ pip install -U numpy\n$ pip install chainercv\n```\n\nThe instruction on installation using Anaconda is [here](http://chainercv.readthedocs.io/en/stable/#install-guide) (recommended).\n\n### Requirements\n\n+ [Chainer](https://github.com/chainer/chainer) and its dependencies\n+ Pillow\n+ Cython (Build requirements)\n\nFor additional features\n\n+ Matplotlib\n+ OpenCV\n+ SciPy\n+ mpi4py\n+ [pycocotools](https://github.com/cocodataset/cocoapi/tree/master/PythonAPI/pycocotools)\n\nEnvironments under Python 2.7.12 and 3.6.0 are tested.\n\n+ The master branch is designed to work on Chainer v6 (the stable version) and v7 (the development version).\n+ The following branches are kept for the previous version of Chainer. Note that these branches are unmaintained.\n    + `0.4.11` (for Chainer v1). It can be installed by `pip install chainercv==0.4.11`.\n    + `0.7` (for Chainer v2). It can be installed by `pip install chainercv==0.7`.\n    + `0.8` (for Chainer v3). It can be installed by `pip install chainercv==0.8`.\n    + `0.10` (for Chainer v4). It can be installed by `pip install chainercv==0.10`.\n    + `0.12` (for Chainer v5). It can be installed by `pip install chainercv==0.12`.\n    + `0.13` (for Chainer v6). It can be installed by `pip install chainercv==0.13`.\n\n# Data Conventions\n\n+ Image\n  + The order of color channel is RGB.\n  + Shape is CHW (i.e. `(channel, height, width)`).\n  + The range of values is `[0, 255]`.\n  + Size is represented by row-column order (i.e. `(height, width)`).\n+ Bounding Boxes\n  + Shape is `(R, 4)`.\n  + Coordinates are ordered as `(y_min, x_min, y_max, x_max)`. The order is the opposite of OpenCV.\n+ Semantic Segmentation Image\n  + Shape is `(height, width)`.\n  + The value is class id, which is in range `[0, n_class - 1]`.\n\n# Sample Visualization\n\n![Example are outputs of detection models supported by ChainerCV](https://user-images.githubusercontent.com/3014172/40634581-bb01f52a-6330-11e8-8502-ba3dacd81dc8.png)\nThese are the outputs of the detection models supported by ChainerCV.\n\n\n# Citation\n\nIf ChainerCV helps your research, please cite the paper for ACM Multimedia Open Source Software Competition.\nHere is a BibTeX entry:\n\n```\n@inproceedings{ChainerCV2017,\n    author = {Niitani, Yusuke and Ogawa, Toru and Saito, Shunta and Saito, Masaki},\n    title = {ChainerCV: a Library for Deep Learning in Computer Vision},\n    booktitle = {ACM Multimedia},\n    year = {2017},\n}\n```\n\nThe preprint can be found in arXiv: https://arxiv.org/abs/1708.08169\n","funding_links":[],"categories":["Neural Networks (NN) and Deep Neural Networks (DNN)","Models/Projects","Python","图像数据与CV"],"sub_categories":["NN/DNN Software Frameworks","Official Add-on Packages"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchainer%2Fchainercv","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchainer%2Fchainercv","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchainer%2Fchainercv/lists"}