Vgg 16 Architecture, Implementation and Practical Use
Step by Step Process to create an Image Classifier Using Vgg16
Hello there, I am Abhay
The era of Convolution Neural Network is at its peak in the 20th century and it is said that it is going to rise more by up to 10% in upcoming years. The reason behind that increase is data. In 2020 according to some resources, it is that that there is almost 90% of data that we used is unlabelled data, and from that 90%, almost 40–50% of data is in the form of images. whether you upload an image on your social media or you upload a post on twitter the image data is everywhere. we need some intelligent algorithms using which we can find the relative meaning from that data.
CNN is mainly used for image classification, segmentation, and also for other co-related fields. a CNN can predict the objects inside an image by just looking at it like we humans do.
The hardest part with CNN is building a CNN. They look great on paper and also when they are implemented, but when you try to implement a CNN it sucks always sucks. There are so many things to handle when we are implementing a CNN-like number of layers, size of the filter, padding type, and more. The solution from all of this is to use a pre-trained model for our image classification project. There are many pre-trained models out there like…
