weiliu89/VGG_ILSVRC_16_layers_fc_reduced_deploy.prototxt Secret

@weiliu89
Why did you change parameters at fc6 and fc7 layers from the original[1]?
For the original VGG-16, fc6's kernel_size should be 7 and the num_output is 4096.

[1]https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md

This is to make VGG faster and also implements the "atrous" algorithm described in DeepLab paper. Here is how the reduced VGG is configured for that: https://gist.github.com/weiliu89/45e9e8de2c13af6476ca#file-vgg_voc2012ext-prototxt-L385

I missed the DeepLab paper -- sorry! ("atrous" algoritm is great!!)

@weiliu89
How can I train this VGG_ILSVRC_16_layers_fc_reduced model?
Any examples or source codes?
Thank you！

Could you provide SSD train.prototxt, deploy.prototxt and solver.txt of InceptionV3?
Thank you.

why the dilation = 3 in fc6?

@shesung, dilation=3, then you can get a true convolutional kernel with size dilation_(kernel_size-1)+1=3_(3-1)+1=7, which is the same with the kernel_size of fc6 in the original VGGnet.

Can i treat dilation equal to the filter_stride in your code of caffe-fcn?

@tron19920125, dilation is not filter_stride.dilation is the hole between your single convolution unit

@weiliu89,What dataset do you use to train ,then get this VGG_ILSVRC_16_layers_fc_reduced.model,because I don't see any data illustrations from VGG_ILSVRC_16_layers_fc_reduced_deploy.prototxt, thank you!

@mjssat7 , using ILSVRC dataset which has 1000 classes.

@weiliu89, I'm trying to understand how do I parse the network output?
I'm using python and get dict with the prob inside. there is a (10,1000,16,16) array.
please any clue.. :)

We fine-tune the basic reduced VGG on ImageNet-1000 dataset or VOC dataset? If ImageNet-1000, Could you report the result of the reduced VGG on ImageNet?

How to create custon dataSet and mark up in on caffe?

@idanusher I guess an average global pooling is needed for the final probability. For 16 x 16 feature maps, avg. global pooling will average all entries in the feature map and return one single value. You can check this in the Caffe Pooling layer.

I can't download the .caffemodel.

I still had a copy of the caffemodel running around. I uploaded it to
https://owncloud.gwdg.de/index.php/s/SjXmX0Uqh1zaYgI/download
checksum should match the one in the table above:
sha1: 97eb7c469c5097f51a0f9a944f4a5731f470eee2
md5: f544332b79d78c838978ce2782b0c196
size: 83 MB

I can't download the .caffemodel.
The URL given in the previous version of this document seems to work, too:

https://vision.cs.unc.edu/wliu/projects/ParseNet/VGG_ILSVRC_16_layers_fc_reduced.caffemodel