VGGNet is a method for object recognition, curated and honed by Oxford University's celebrated Visual Geometry Group (VGG). It gained recognition for its impressive performance on the ImageNet dataset, significantly surpassing other rival algorithms. The fame of VGGNet extends not just due to its efficacy, but also due to the developers' decision to publicly share the network's structure and weights online.
VGG-19 characterizes an iteration of this system that possesses a depth of 19 layers, making it a profound convolutional neural network. During the 2014 ILSVRC, VGGNet emerged victorious in image localization and runner up in the image classification task. Localization identifies the precise region of an image where a particular object is housed, whereas classification involves identifying and labeling the object within the image, such as "automobile" or "feline".
ImageNet, an extensive image database maintained by academic researchers, holds an annual image recognition contest. Contestants are required to devise a software solution, commonly a neural network, capable of accurately categorizing a set of test images. The database contains 1000 distinct categories. The contest's neural network develops a probability distribution for each image. It assigns a probability score to each of the categories, finally selecting the one with the maximum score.
The ImageNet competition grants each classification attempt five opportunities to predict the correct category, which is why the demo showcases the top five predictions of the network.
VGGNet utilizes an input dimension of 224x224 pixels RGB image. To maintain input consistency for ImageNet, a 224×224 patch from the middle of every image is cropped. All VGG hidden layers utilize ReLU to speed up training without using Local Response Normalization (LRN) that burdens memory and slows down training.
VGGNet incorporates three fully connected layers, with the initial two layers having 4096 channels and the final layer having 1000 channels corresponding to each class.
Comparison with AlexNet
In contrast, AlexNet comprises eight layers, including five convolutional layers, three fully connected layers, and LRN layers post the first, second, and fifth convolutional layers. AlexNet also includes 96 11x11 filters.
VGG-16, with its 16 layers including 13 convolutional layers and fully connected layers, exhibits higher complexity as compared to AlexNet. The stride and padding for both structures' convolutional layers are set to 1 pixel.
VGGNet's Design Approach
VGGNet employs small receptive fields such as 3x3 with a stride of 1, ensuring higher discrimination in the decision function and lesser parameters as compared to AlexNet. Simultaneously, it uses 1x1 convolutional layers to increase non-linearity in the decision function. Even with the reduction in the size of convolution filters, VGGNet manages to accommodate a significant number of weight layers, thereby enhancing its performance.
VGGNet's Simplicity and Functionality
VGG architecture under vis-a-vis proven deep learning models like AlexNet is characterized by its use of smaller 3x3 filters. The architecture emphasizes simplicity and comprehensive functionality, using just pooling layers and a fully connected layer for additional components. To this day, VGGNet remains a widely used image-recognition model.