What is GoogLeNet?

GoogLeNet, also known as Inception Net, is a convolutional neural network (CNN) developed by researchers at Google. It is a 22-layer deep architecture and was trained on the ImageNetImageNet is a large-scale dataset that contains millions of labeled images spanning thousands of different categories for data collection in computer vision tasks. dataset. It can classify objects into 1,000 different categories.

Note: GoogLeNet was first proposed in a research paperSzegedy, Christian, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, and W. Liu. "Going deeper with convolutions. arXiv 2014." arXiv preprint arXiv:1409.4842 12. published in 2014.

Architecture

The GoogLeNet architecture is based on building a deeper model to achieve greater accuracy while keeping it computationally efficient. Neural networks with deeper architectures can capture complex patterns and extract hierarchical features which helps in generalizing better to new, unseen data.

• Global average pooling: It is used at the end of the GoogLeNet architecture before the fully connected layer to reduce the spatial dimensions of input, which helps reduce computational costs. It also helps reduce overfitting.

• Inception module: It uses multiple filter sizes (5x5, 3x3, 1x1) to extract features at different scales and concatenates their output into a single output. The diagram of the inception module is given below.

• Auxiliary classifier: When training datasets on a deep learning model, the common problem is the vanishing gradientIt occurs during the training of deep neural networks, where the gradients that are used to update the network become extremely small or "vanish" as they are backpropogated from the output layers to the earlier layers. problem. Auxiliary classifiers are used at intermediate layers of the GoogLeNet architecture to prevent the vanishing gradient problem during training. The diagram of the auxiliary classifier is given below.

The complete architecture of the GoogLeNet architecture is given below.

Key features

• Efficiency: The architecture of GoogLeNet consists of many 1x1 filters, which reduce the number of parameters and make the model highly efficient.

• Accuracy: GoogLeNet achieved state-of-the-art accuracy on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset in 2014.

• Vanishing gradient: GoogLeNet uses auxiliary classifiers to address the vanishing gradient problem.

• Transfer learning: GoogLeNet is trained on the ImageNet dataset and can classify 1,000 objects. This can be used to boost the performance of a related task.