From the course: Deep Learning with Python: Convolutional Neural Networks
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VGGNet - Python Tutorial
From the course: Deep Learning with Python: Convolutional Neural Networks
VGGNet
- [Instructor] VGGNet is a widely recognized convolutional neural network architecture introduced in 2014 by Karen Simonyan and Andrew Zisserman at the University of Oxford's Visual Geometry Group. It gained prominence for its outstanding performance in the 2014 ImageNet Large Scale Visual Recognition Challenge where it placed second in the image classification category. What set VGGNet apart was a straightforward, yet powerful design. Instead of using large or varied filters, the model used stacks of small three by three convolutional filters. This approach allowed the network to become deeper, while maintaining manageable complexity leading to rich hierarchical feature representations and strong performance across various vision tasks. One of the primary strengths of VGGNet lies in its simplicity and uniform structure. The model is built almost entirely from repeated blocks of three by three convolutional layers, which ReLU activation followed occasionally by max pulling layers…
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