Skip to main content

PyTorch Encoding Package

Project description

PyPI PyPI Pre-release Upload Python Package Downloads License: MIT Build Docs Unit Test

PyTorch-Encoding

created by Hang Zhang

Documentation

  • Please visit the Docs for detail instructions of installation and usage.

  • Please visit the link to image classification models.

  • Please visit the link to semantic segmentation models.

Citations

ResNeSt: Split-Attention Networks [arXiv]
Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Muller, R. Manmatha, Mu Li and Alex Smola

@article{zhang2020resnest,
title={ResNeSt: Split-Attention Networks},
author={Zhang, Hang and Wu, Chongruo and Zhang, Zhongyue and Zhu, Yi and Zhang, Zhi and Lin, Haibin and Sun, Yue and He, Tong and Muller, Jonas and Manmatha, R. and Li, Mu and Smola, Alexander},
journal={arXiv preprint},
year={2020}
}

Context Encoding for Semantic Segmentation [arXiv]
Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, Amit Agrawal

@InProceedings{Zhang_2018_CVPR,
author = {Zhang, Hang and Dana, Kristin and Shi, Jianping and Zhang, Zhongyue and Wang, Xiaogang and Tyagi, Ambrish and Agrawal, Amit},
title = {Context Encoding for Semantic Segmentation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}

Deep TEN: Texture Encoding Network [arXiv]
Hang Zhang, Jia Xue, Kristin Dana

@InProceedings{Zhang_2017_CVPR,
author = {Zhang, Hang and Xue, Jia and Dana, Kristin},
title = {Deep TEN: Texture Encoding Network},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

torch-encoding-1.2.0b20200422.tar.gz (75.3 kB view details)

Uploaded Source

Built Distribution

torch_encoding-1.2.0b20200422-py2.py3-none-any.whl (117.0 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file torch-encoding-1.2.0b20200422.tar.gz.

File metadata

  • Download URL: torch-encoding-1.2.0b20200422.tar.gz
  • Upload date:
  • Size: 75.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.45.0 CPython/3.7.6

File hashes

Hashes for torch-encoding-1.2.0b20200422.tar.gz
Algorithm Hash digest
SHA256 ba3104fce0d74906a285812812feed93526c048c872723485dcaf38fb34acdf7
MD5 2444ecab32619fc5d0f94cb5bed46caa
BLAKE2b-256 363309c1d70c6e30694489dc36eedc9f355d2eb104f10c03f17361687eaa397b

See more details on using hashes here.

File details

Details for the file torch_encoding-1.2.0b20200422-py2.py3-none-any.whl.

File metadata

  • Download URL: torch_encoding-1.2.0b20200422-py2.py3-none-any.whl
  • Upload date:
  • Size: 117.0 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.45.0 CPython/3.7.6

File hashes

Hashes for torch_encoding-1.2.0b20200422-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 5413cf56d1d0cf3e4ba30815359449d5ac2791a06d6596e0810ce727ce79eb67
MD5 d96243936638d2e6213b2e0ffa7a9e1e
BLAKE2b-256 04a27dcc19638413dee70af503871670f754814a6a202d32aaff150ad007d0a9

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page