How many parameters does GoogLeNet have?

How many parameters does GoogLeNet have?

GoogleNet possesses seven million parameters and contains nine inception modules, four convolutional layers, four max-pooling layers, three average pooling layers, five fully-connected layers, and three softmax layers for the main auxiliary classifiers in the network [33].

What is GoogLeNet?

GoogLeNet is a convolutional neural network that is 22 layers deep. You can load a pretrained version of the network trained on either the ImageNet [1] or Places365 [2] [3] data sets. The network trained on ImageNet classifies images into 1000 object categories, such as keyboard, mouse, pencil, and many animals.

Why is GoogLeNet used?

Today GoogLeNet is used for other computer vision tasks such as face detection and recognition, adversarial training etc.

Is GoogLeNet and inception same?

Inception V1 (or GoogLeNet) was the state-of-the-art architecture at ILSRVRC 2014. It has produced the record lowest error at ImageNet classification dataset but there are some points on which improvement can be made to improve the accuracy and decrease the complexity of the model.

What is AlexNet and GoogLeNet?

AlexNet has parallel two CNN line trained on two GPUs with cross-connections, GoogleNet has inception modules ,ResNet has residual connections.

What is the main innovation in GoogLeNet?

The main novelty in the architecture of GoogLeNet is the introduction of a particular module called Inception.

What kind of architecture is GoogLeNet?

GoogLeNet is a type of convolutional neural network based on the Inception architecture. It utilises Inception modules, which allow the network to choose between multiple convolutional filter sizes in each block.

What is AlexNet and GoogleNet?

What kind of architecture is GoogleNet?

What is inception in Googlenet?

Inception v3 is a convolutional neural network for assisting in image analysis and object detection, and got its start as a module for Googlenet. It is the third edition of Google’s Inception Convolutional Neural Network, originally introduced during the ImageNet Recognition Challenge.

What is Inception module in Googlenet?

The main idea of the Inception module is that of running multiple operations (pooling, convolution) with multiple filter sizes (3×3, 5×5…) in parallel so that we do not have to face any trade-off. Then, three operations are carried out in parallel: a convolutional operation with 16 filters of size 1×1.

Is GoogLeNet better than AlexNet?

According to the results of the experiment, GoogLeNet training on fabric defects is faster than that of AlexNet. The performance of GoogLeNet is the best outdoing than AlexNet on various parameter including time, accuracy, dropout, and the initial learning.

What is GoogLeNet and how to use it?

GoogLeNet is now a staple architecture within most common ML libraries such as TensorFlow, Keras, PyTorch etc. And with the use of transfer learning, you can utilise a GoogLeNet network trained on imagenet without implementing or training the network yourself.

What is the difference between AlexNet and GoogLeNet?

On this chapter you will learn about the googleNet (Winning architecture on ImageNet 2014) and it’s inception layers. googleNet has 22 layer, and almost 12x less parameters (So faster and less then Alexnet and much more accurate.

What is global average pooling in GoogLeNet?

In GoogLeNet architecture, there is a method called global average pooling is used at the end of the network. This layer takes a feature map of 7×7 and averages it to 1×1. This also decreases the number of trainable parameters to 0 and improves the top-1 accuracy by 0.6%

What is the size of the input layer in GoogLeNet?

The input layer of the GoogLeNet architecture takes in an image of the dimension 224 x 224. Type: This refers to the name of the current layer of the component within the architecture Patch Size: Refers to the size of the sweeping window utilised across conv and pooling layers. Sweeping windows have equal height and width.