Caffe Wide-Residual-Network (WRN) Generator
===========================================
This generator is a reimplementation of Wide Residual Networks (WRN) [[1]](https://github.com/razorx89/caffe-wrn-generator/blob/master/#ref1).
Full-Pre-Activation Residual Units from [[2]](https://github.com/razorx89/caffe-wrn-generator/blob/master/#ref2) are used with two
convolutional units of size 3x3 per residual unit. Bottleneck
residual units (3 convolutional layers: 1x1, 3x3, 1x1) are available by using `--bottleneck-resunit`.
Currently the generator is implemented for CIFAR-10/CIFAR-100 (32x32 pixels) and ImageNet
(224x224 pixels).
How to use
----------
The generator expects a list of residual unit counts per spatial resolution. For CIFAR-10/CIFAR-100
there are 3 spatial resolutions, for ImageNet 4 spatial resolutions with residual units.
__WRN-16-4 for CIFAR-10:__
Command: `python generate.py cifar10 2,2,2 4`
Output: cifar10_WRN-16-4_train_val.prototxt
__WRN-16-4 with Dropout for CIFAR-100:__
Command: `python generate.py cifar100 2,2,2 4 --dropout=0.3`
Output: cifar100_WRN-16-4_dropout_train_val.prototxt
__WRN-53-2 for ImageNet with Bottleneck Residual Units:__
Command: `python generate.py imagenet 3,4,6,3 2 --bottleneck-resunit`
Output: imagenet_WRN-53-2_bottleneck_train_val.prototxt
For more customization options check the possible arguments with
`python generate.py --help`.
Notes
-----
* First release only used BatchNormLayer without ScaleLayer
References
----------
-
[1] Sergey Zagoruyko, Nikos Komodakis; "Wide Residual
Networks"; British Machine Vision Conference (BMVC) 2016, 19-22 September,
York, UK; 2016; [arXiv](https://github.com/razorx89/caffe-wrn-generator/blob/master/https://arxiv.org/abs/1605.07146),
[Github](https://github.com/razorx89/caffe-wrn-generator/blob/master/https://github.com/szagoruyko/wide-residual-networks)
-
[2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun;
"Identity Mappings in Deep Residual Networks", arXiv preprint arXiv:1603.05027,
2016; [arXiv](https://github.com/razorx89/caffe-wrn-generator/blob/master/https://arxiv.org/abs/1603.05027),
[Github](https://github.com/razorx89/caffe-wrn-generator/blob/master/https://github.com/KaimingHe/resnet-1k-layers)
Visualization of a WRN-16-4 with Dropout
-----------------------------
