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.
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.
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.
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.