What is AlexNet and GoogLeNet?

GoogleNet identifies the fabric defect in 33 seconds with 100% accuracy for 204 images, whereas AlexNet takes 54 seconds with less accuracy of 90% for the same number of images. The dropout value for AlexNet is 0.5 which increases the training time of the network.

Is GoogLeNet a CNN?

GoogLeNet is a 22-layer deep convolutional neural network that’s a variant of the Inception Network, a Deep Convolutional Neural Network developed by researchers at Google.

What is GoogLeNet network?

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 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.

Is GoogLeNet better than ResNet?

Through the changes mentioned, ResNets were learned with network depth of as large as 152. It achieves better accuracy than VGGNet and GoogLeNet while being computationally more efficient than VGGNet. ResNet-152 achieves 95.51 top-5 accuracies. The architecture is similar to the VGGNet consisting mostly of 3X3 filters.

Why is ResNet preferred over GoogLeNet?

Is inception and GoogLeNet same?

Using the dimension-reduced inception module, a neural network architecture is constructed. This is popularly known as GoogLeNet (Inception v1). GoogLeNet has 9 such inception modules fitted linearly. It is 22 layers deep (27, including the pooling layers).

Does GoogLeNet use global average pooling?

Features of GoogleNet: It uses many different kinds of methods such as 1×1 convolution and global average pooling that enables it to create deeper architecture.

What is AlexNet good for?

AlexNet is a leading architecture for any object-detection task and may have huge applications in the computer vision sector of artificial intelligence problems. In the future, AlexNet may be adopted more than CNNs for image tasks.

Why is AlexNet so good?

Conclusion. AlexNet is a work of supervised learning and got very good results. It is not easy to have low classification errors without having of overfitting. They say that removing one convolutional layer from their network would reduce drastically the performance so its no easy task to choose the architecture.

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.

What is the difference between AlexNet and Google’s VGG?

And the original proposed VGG network was much deeper than the AlexNet. 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.

What is the difference between GoogLeNet and resnet and AlexNet?

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

Can I use GoogLeNet and AlexNet with Keras?

2 Answers 2 ActiveOldestVotes 1 For Googlenet you can use this model. GoogLeNet in Keras. For Alexnet Building AlexNet with Keras. The problem is you can’t find imagenet weights for this model but you can train this model from zero.

What is the architecture of GoogLeNet?

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.