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computer science

VGGNet

VGGNet is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand VGGNet rather than just read about it. In short: The VGGNets are a series of convolutional neural networks (CNNs) developed by the Visual Geometry Group (VGG) at the University of Oxford. The VGG family includes various configurations with different depths, denoted by the letter "VGG" followed by the number of weight layers.

VGGNet — main illustration
VGGNet — illustration

Key takeaways

  • VGGNet belongs to computer science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect VGGNet to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of VGGNet from memory before moving on to harder problems.

Reference excerpt

The VGGNets are a series of convolutional neural networks (CNNs) developed by the Visual Geometry Group (VGG) at the University of Oxford. The VGG family includes various configurations with different depths, denoted by the letter "VGG" followed by the number of weight layers. The most common ones are VGG-16 (13 convolutional layers + 3 fully connected layers, 138M parameters) and VGG-19 (16 + 3, 144M parameters). The VGG family were widely applied in various computer vision areas. An ensemble model of VGGNets achieved state-of-the-art results in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2014. It was used as a baseline comparison in the ResNet paper for image classification, as the network in the Fast Region-based CNN for object detection, and as a base network in neural style transfer. The series was historically important as an early influential model designed by composing generic modules, whereas AlexNet (2012) was designed "from scratch". It was also instrumental in changing the standard convolutional kernels in CNN from large (up to 11-by-11 in AlexNet) to just 3-by-3, a decision that was only revised in ConvNext (2022). VGGNets were rendered obsolete by Inception, ResNet, and DenseNet. RepVGG (2021) is an updated version of the architecture.

Architecture The key architectural principle of VGG models is the consistent use of small 3 × 3 {\displaystyle 3\times 3} convolutional filters throughout the network. This contrasts with earlier CNN architectures that employed larger filters, such as 11 × 11 {\displaystyle 11\times 11} in AlexNet. For example, two 3 × 3 {\textstyle 3\times 3} convolutions stacked together has the same receptive field pixels as a single 5 × 5 {\textstyle 5\times 5} convolution, but the latter uses ( 25 ⋅ c 2 ) {\textstyle \left(25\cdot c^{2}\right)} parameters, while the former uses ( 18 ⋅ c 2 ) {\textstyle \left(18\cdot c^{2}\right)} parameters (where c {\displaystyle c} is the number of channels). The original publication showed that deep and narrow CNN significantly outperform their shallow and wide counterparts. The VGG series of models are deep neural networks composed of generic modules:

… excerpt ends here. Continue reading the full article.

Illustrations

VGGNet: VGG module architecture compared to AlexNet architecture
VGG module architecture compared to AlexNet architecture
VGGNet: Network-in-Network architecture compared to the VGG architecture. The Network in Network architecture (2013)[9] was an earlier CNN. It changed the AlexNet architecture by adding 1x1 convolutions, and using a global average pooling after the last convolution.
Network-in-Network architecture compared to the VGG architecture. The Network in Network architecture (2013)[9] was an earlier CNN. It changed the AlexNet architecture by adding 1x1 convolutions, and using a global average pooling after the last convolution.

Worked examples

Example 1 — a first encounter with VGGNet

Start with the simplest possible case. Write down what VGGNet claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to VGGNet before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about VGGNet ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of VGGNet

In research
VGGNet appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses VGGNet in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
VGGNet is common in secondary-school and first-year university syllabi. It links to neighbouring topics Deep learning software, Neural network architectures, Object recognition and categorization, so understanding it makes those chapters shorter.
In everyday life
Look for VGGNet outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study VGGNet in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what VGGNet means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain VGGNet out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is VGGNet in simple terms?

The VGGNets are a series of convolutional neural networks (CNNs) developed by the Visual Geometry Group (VGG) at the University of Oxford. The VGG family includes various configurations with different depths, denoted by the letter "VGG" followed by the number of weight layers.

Why does VGGNet matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study VGGNet?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on VGGNet.

Tags

  • Deep learning software
  • Neural network architectures
  • Object recognition and categorization

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