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add versatile filters
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iamhankai committed Aug 31, 2021
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15 changes: 14 additions & 1 deletion readme.md
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Expand Up @@ -4,11 +4,14 @@ including GhostNet, TinyNet, TNT (Transformer in Transformer) developed by Huawe
- [TinyNet Code](#tinynet-code)
- [TNT Code](#tnt-code)
- [LegoNet Code](#legonet-code)
- [Versatile Filters Code](#versatile-filters-code)
- [Citation](#citation)
- [Other versions](#other-versions)

**News**

2021/08/31 The code of Versatile Filters (https://github.com/huawei-noah/Versatile-Filters) has been merged into this repo.

2021/08/26 The code of LegoNet (https://github.com/huawei-noah/LegoNet) has been merged into this repo.

2021/06/15 The code of TNT (Transformer in Transformer) has been released in this repo.
Expand Down Expand Up @@ -45,9 +48,13 @@ This repo provides **training code** of TNT (Transformer in Transformer) for PyT
- We also opensource code on [MindSpore Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/TNT).

## LegoNet Code
This repo provides the implementation of ICML2019 paper [LegoNet: Efficient Convolutional Neural Networks with Lego Filters](http://proceedings.mlr.press/v97/yang19c/yang19c.pdf)
This repo provides the implementation of paper [LegoNet: Efficient Convolutional Neural Networks with Lego Filters (ICML 2019)](http://proceedings.mlr.press/v97/yang19c/yang19c.pdf)
- PyTorch: [./legonet_pytorch](https://github.com/huawei-noah/CV-backbones/tree/master/legonet_pytorch).

## Versatile Filters Code
This repo provides the implementation of paper [Learning Versatile Filters for Efficient Convolutional Neural Networks (NeurIPS 2018)](https://papers.nips.cc/paper/7433-learning-versatile-filters-for-efficient-convolutional-neural-networks)
- PyTorch: [./versatile_filters](https://github.com/huawei-noah/CV-backbones/tree/master/versatile_filters).


## Citation
```
Expand Down Expand Up @@ -75,6 +82,12 @@ This repo provides the implementation of ICML2019 paper [LegoNet: Efficient Conv
booktitle={ICML},
year={2019}
}
@inproceedings{wang2018learning,
title={Learning versatile filters for efficient convolutional neural networks},
author={Wang, Yunhe and Xu, Chang and Chunjing, XU and Xu, Chao and Tao, Dacheng},
booktitle={NeurIPS},
year={2018}
}
```

## Other versions of GhostNet
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13 changes: 13 additions & 0 deletions versatile_filters/License.txt
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Copyright (c) 2019. Huawei Technologies Co., Ltd.
All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

74 changes: 74 additions & 0 deletions versatile_filters/THIRD PARTY OPEN SOURCE SOFTWARE NOTICE.txt
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Please note we provide an open source software notice for the third party open source software along with this software and/or this software component contributed by Huawei (in the following just “this SOFTWARE”). The open source software licenses are granted by the respective right holders.

Warranty Disclaimer
THE OPEN SOURCE SOFTWARE IN THIS SOFTWARE IS DISTRIBUTED IN THE HOPE THAT IT WILL BE USEFUL, BUT WITHOUT ANY WARRANTY, WITHOUT EVEN THE IMPLIED WARRANTY OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. SEE THE APPLICABLE LICENSES FOR MORE DETAILS.

Copyright Notice and License Texts
Software: PyTorch Examples 0.4
Copyright notice:
Copyright (c) 2017

License: BSD 3-Clause License

Copyright (c) 2017,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Copyright Notice and License Texts
Software: torchvision 0.2.0
Copyright notice:
Copyright (c) Soumith Chintala 2016

License: BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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