VGG Architecture

Описание: @genaiexp The VGG architecture, developed by the Visual Geometry Group from the University of Oxford, is renowned for its pioneering depth. VGG models, particularly VGG16 and VGG19, are celebrated for their simplicity and effectiveness in image classification tasks. The architecture uses very small 3x3 convolutional filters, which stack multiple layers to increase the depth of the network. VGG16 consists of 16 weight layers, whereas VGG19 includes 19. This depth allows the network to learn more complex features, albeit with increased computational cost and memory requirements. Despite these drawbacks, VGG remains a popular choice due to its straightforward design and impressive performance on benchmark datasets. It is widely used in tasks like object recognition, where detailed feature extraction is crucial.