├── polynet.png
├── LICENSE
├── .gitignore
├── README.md
└── compare.svg
/polynet.png:
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https://raw.githubusercontent.com/MMLab-CU/polynet/HEAD/polynet.png
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/LICENSE:
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1 | MIT License
2 |
3 | Copyright (c) 2017 Multimedia Laboratory, CUHK
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
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9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
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/.gitignore:
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1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | env/
12 | build/
13 | develop-eggs/
14 | dist/
15 | downloads/
16 | eggs/
17 | .eggs/
18 | lib/
19 | lib64/
20 | parts/
21 | sdist/
22 | var/
23 | *.egg-info/
24 | .installed.cfg
25 | *.egg
26 |
27 | # PyInstaller
28 | # Usually these files are written by a python script from a template
29 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
30 | *.manifest
31 | *.spec
32 |
33 | # Installer logs
34 | pip-log.txt
35 | pip-delete-this-directory.txt
36 |
37 | # Unit test / coverage reports
38 | htmlcov/
39 | .tox/
40 | .coverage
41 | .coverage.*
42 | .cache
43 | nosetests.xml
44 | coverage.xml
45 | *,cover
46 | .hypothesis/
47 |
48 | # Translations
49 | *.mo
50 | *.pot
51 |
52 | # Django stuff:
53 | *.log
54 | local_settings.py
55 |
56 | # Flask stuff:
57 | instance/
58 | .webassets-cache
59 |
60 | # Scrapy stuff:
61 | .scrapy
62 |
63 | # Sphinx documentation
64 | docs/_build/
65 |
66 | # PyBuilder
67 | target/
68 |
69 | # IPython Notebook
70 | .ipynb_checkpoints
71 |
72 | # pyenv
73 | .python-version
74 |
75 | # celery beat schedule file
76 | celerybeat-schedule
77 |
78 | # dotenv
79 | .env
80 |
81 | # virtualenv
82 | venv/
83 | ENV/
84 |
85 | # Spyder project settings
86 | .spyderproject
87 |
88 | # Rope project settings
89 | .ropeproject
90 |
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/README.md:
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1 | # PolyNet: A Pursuit of Structural Diversity in Very Deep Networks
2 |
3 | By [Xingcheng Zhang](https://www.linkedin.com/in/xingchengzhang/), [Zhizhong Li](http://mmlab.ie.cuhk.edu.hk/html_people/postgraduate_Zhizhong_Li.html), [Chen Change Loy](http://personal.ie.cuhk.edu.hk/~ccloy/), [Dahua Lin](http://dahua.me)
4 |
5 | [Multimedia Laboratory](http://mmlab.ie.cuhk.edu.hk), The Chinese University of Hong Kong
6 |
7 | [](http://mmlab.ie.cuhk.edu.hk/projects/cu_deeplink/)
8 | [](https://arxiv.org/abs/1611.05725)
9 |
10 | ### BibTeX
11 |
12 | ```bib
13 | @article{zhang2016polynet,
14 | title={Polynet: A pursuit of structural diversity in very deep networks},
15 | author={Zhang, Xingcheng and Li, Zhizhong and Loy, Chen Change and Lin, Dahua},
16 | journal={arXiv preprint arXiv:1611.05725},
17 | year={2016}
18 | }
19 | ```
20 |
21 | ### The Very Deep PolyNet
22 | 
23 | * [Visualization](http://ethereon.github.io/netscope/#/gist/b22923712859813a051c796b19ce5944)
24 | * Models
25 | * **Parrots**: model files to be added;
26 | * [Caffe](https://github.com/yjxiong/caffe): [proto](https://drive.google.com/open?id=0B6pxsvrUJ931aTJmdEJOODhtQ3c), [model](https://drive.google.com/open?id=0B6pxsvrUJ931WXluclRBVDAtaEk).
27 |
28 | NOTE: The model is trained using our own deep learning framework **Parrots**. The caffe model is converted from the **Parrots** model, and the proto file is based on [Yuanjun's fork](https://github.com/yjxiong/caffe) of Caffe.
29 |
30 | ### Results
31 |
32 | 
33 |
34 | model|training speed* (#imgs/second)|single-crop val top-1|single-crop val top-5|single-crop test top-5 | multi-crop val top-1 | multi-crop val top-5
35 | :---:|:---:|:---:|:---:|:---:|:---:|:---:
36 | [ResNet-152](https://github.com/KaimingHe/deep-residual-networks) |-| [22.16](https://github.com/facebook/fb.resnet.torch#single-crop-224x224-validation-error-rate) | [6.16](https://github.com/facebook/fb.resnet.torch#single-crop-224x224-validation-error-rate) | - | [19.38](https://arxiv.org/pdf/1512.03385.pdf) | [4.49](https://arxiv.org/pdf/1512.03385.pdf)
37 | ResNet-152^ |279 ( 8 GPUs)| 20.93 | 5.54 | 5.50 | 18.50 | 3.97
38 | ResNet-269^ |245 (16 GPUs)| 19.78 | 4.89 | 4.82 | 17.54 | 3.55
39 | ResNet-500^ |248 (32 GPUs)| 19.66 | 4.78 | 4.70 | 17.59 | 3.63
40 | [Inception-v4](https://arxiv.org/abs/1602.07261) |-| 20.0 | 5.0 | -| 1 7.7 | 3.8
41 | [Inception-ResNet-v2](https://arxiv.org/abs/1602.07261) |-| 19.9 | 4.9 | - | 17.8 | 3.7
42 | Inception-ResNet-v2 |314 ( 8 GPUs)| 20.05 | 5.05 | 5.11 | 18.41 | 3.98
43 | Very Deep Inception-ResNet |278 (32 GPUs)| 19.10 | 4.48 | 4.46 | 17.39 | 3.56
44 | Very Deep PolyNet |290 (32 GPUs)| **18.71** | **4.25** | **4.33** | **17.36** | **3.45**
45 |
46 | ^ The ResNet models are trained by [Tong Xiao](https://github.com/Cysu);
47 |
48 | \* Training speed is measured on **Parrots** using NVIDIA TITAN X Graphics Cards.
49 |
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