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What is VGGNet?

Its object recognition method developed and trained by Oxford’s renowned VGG (Visual Geometry Group), which outperformed the ImageNet dataset by a wide margin.

It is well-known not only because it works effectively, but also because the Oxford team has made the trained network’s structure and weights publicly available online.

  • VGG-19 is a 19-layer deep convolutional neural network.

In the 2014 ILSVRC, VGG neural network architecture took first place in the image localization task and second place in the image classification task.

Finding the location of a certain object in an image, as defined by a bounding box, is known as localization. The term “classification” refers to the process of describing what the object in the image is. This indicates the presence of a category label, such as “dog” or “vehicle.”


For academic scholars, ImageNet is a massive image database. The people that manage ImageNet hold an image recognition competition every year. The goal is to create software — usually a neural network of some form these days — that can properly predict the category for a collection of test photos. Of course, only the contest organizers are aware of the exact categories.

The competition’s images are sorted into 1000 separate categories. The neural network will generate a probability distribution for a given test image. This means it calculates a probability for each of the 1000 categories (a number between 0 and 1), then chooses the category with the highest probability.

The neural network’s top pick has a high probability if it is very certain about a prediction.

In the ImageNet classification task, you have five chances to estimate the correct category, which is why the demo app displays the network’s top five possibilities.

VGG architecture

  • Input VGG neural network accepts a 224×224 pixel RGB image as input. To keep the input image size consistent for the ImageNet competition, the authors clipped out the middle 224×224 patch in each image.
  • Convolutional Layers have a 3×3 receptive field, which is the smallest size achievable while still capturing left/right and up/down. There are additional convolution filters that perform a linear change of the input before passing it through a ReLU unit. The stride is set at 1 pixel in order to preserve spatial resolution following convolution.
  • VGG’s hidden layers all use ReLU, an AlexNet invention that significantly reduces training time. Local Response Normalization (LRN) is not used by VGG since it increases memory usage and training time without improving accuracy.
  • VGG contains three completely connected layers, the first two of which each has 4096 channels and the third of which has 1000 channels, one for each class.

On the other hand, AlexNet is made up of eight layers, including five convolutional layers, three totally connected levels, and three layers following the 1st, 2nd, and 5th convolutional layers. The first convolutional layer is made up of 96 11 x 11 filters with a 4-pixel stride and 2-pixel padding. Stride and padding for the other convolutional layers are all set to 1 pixel.

With 16 layers and 13 convolutional layers and entirely linked layers, the VGG 16 is far more complicated. VGG -16 and AlexNet both have the same fully linked layer setups. 1 pixel is stride and padding for both convolutional layers. Each layer is separated into 5 groups.

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Advantages of VGG

  • VGG uses very small receptive fields instead of massive fields like AlexNet. So it uses 3×3 with a stride of 1. The decision function is more discriminative now that there are three ReLU units instead of simply one. There are also fewer parameters (27 times the number of channels vs. 49 times the number of channels in AlexNet).
  • Without modifying the receptive fields, VGG uses 1×1 convolutional layers to make the decision function more non-linear.
  • VGG model can have a considerable number of weight layers due to the small size of the convolution filters; of course, more layers mean better performance. However, this isn’t an unusual trait.

The VGG architecture is a convolutional neural network architecture that has been around for a while. It was developed as a result of research on how to make specific networks denser. The network employs tiny 3 x 3 filters. Aside from that, the network stands out for its simplicity, with simply pooling layers and a fully linked layer as extra components. VGG net deep learning model is one of the most widely employed image-recognition models today.


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