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 are the major differences between the inception block and the residual block?

The difference is the batch normalization layer added after each convolutional layer in ResNet. GoogLeNet uses four modules made up of Inception blocks. However, ResNet uses four modules made up of residual blocks, each of which uses several residual blocks with the same number of output channels.

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 in CNN?

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.

Is GoogLeNet and inception v3 same?

GoogleNet has a quite different architecture than both: it uses combinations of inception modules, each including some pooling, convolutions at different scales and concatenation operations. It also uses 1×1 feature convolutions that work like feature selectors. 1.

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 AlexNet a 3D CNN?

3D AlexNet Network with a standart AlexNet architecture, but it has 3D instead 2D filters on each Conv and Pool layers.

https://www.youtube.com/watch?v=CNNnzl8HIIU