What is https://en.wikipedia.org/wiki/VGG and why does the reader need to guess?

It's the implementation of https://arxiv.org/abs/1409.1556 a classification network

Hey @jeffin07,
Your changes look good to me!

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TravisBuddy Request Identifier: 7110f230-a9a7-11ea-9161-4f77e253115e

Hey @jeffin07,
Your changes look good to me!

View build log

TravisBuddy Request Identifier: 28f2ebb0-a9a8-11ea-9161-4f77e253115e

It's the implementation of https://arxiv.org/abs/1409.1556 a classification network

The problem is that the URL does not contain the acronym VGG and the code comments do not explain what VGG stands for. It is going to confuse the reader to have class without knowing what the class name stands for.

Hey @jeffin07,
Your changes look good to me!

View build log

TravisBuddy Request Identifier: 4b310660-a9aa-11ea-9161-4f77e253115e

@jeffin07 You can change the VGG to VGG-16/19 term as they are mainly the standard ones and people refer to them mostly.

@cclauss VGG is a CNN classification, you can get it by using VGG-16/19.

There is one problem though, the PR contains the model defination using pytorch and no insights to the layers(their functionality) but am not so sure that if you could call this as a work in algorithms.

• What does the V stand for?
• What does the first G stand for?
• What does the second G stand for?

VGG(Visual Geometry Group) or also OxfordNet. Similarly there are AlexNet or ResNET, etc

Thanks, please change the filename to visual_geometry_group.py or oxford_net.py.

@cclauss Actually VGG is often used and using a VGG-16/19 model and renaming the file as vgg_16 / vgg_19 would be beneficial, like using kmp instead of Knuth Morris Pratt.

In this repo we call it Knuth Morris Pratt instead of kmp. We do that very deliberately because we are trying to teach the readers of our code. We avoid unexplained acronyms because we want our readers to understand the origins and nomenclature of these algorithms. The 16 and 19 are config choices that have proven to be effective but they are not the algorithm. https://www.robots.ox.ac.uk/~vgg/research/very_deep