VGG Insights | Sumble

What is VGG?

VGG (Visual Geometry Group) is a convolutional neural network architecture notable for its depth and uniform structure. It primarily uses 3x3 convolutional filters and 2x2 max pooling layers throughout the entire network. VGG models, such as VGG16 and VGG19, are commonly used as feature extractors in image classification, object detection, and image segmentation tasks. They have also been used as a backbone for more complex architectures and transfer learning due to their robust and well-understood features.

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What other technologies are related to VGG?

VGG Competitor Technologies

ResNet

ResNet is a competing deep learning architecture that improves upon VGG by introducing residual connections, allowing for deeper networks and better performance.

mentioned alongside VGG in 12% (113) of relevant job posts

YOLO

YOLO is a competing real-time object detection system that offers an alternative approach to object detection compared to methods using VGG as a base network.

mentioned alongside VGG in 2% (51) of relevant job posts

VGG Complementary Technologies

OpenCV

OpenCV is a library of programming functions mainly aimed at real-time computer vision. It can be used in conjunction with VGG for image processing tasks like pre-processing or post-processing of model outputs.

mentioned alongside VGG in 0% (63) of relevant job posts

PyTorch

PyTorch is a deep learning framework that can be used to implement and train VGG models, thus serving as a complementary technology.

mentioned alongside VGG in 0% (108) of relevant job posts

TensorFlow

TensorFlow is a deep learning framework that can be used to implement and train VGG models, thus serving as a complementary technology.

mentioned alongside VGG in 0% (82) of relevant job posts