It is used to process the data represented as a 2D matrix like images. These extracted features will be fed to the LSTM model which in turn generates the image caption.ĬNN is a subfield of Deep learning and specialized deep neural networks used for the recognition and classification of images. Xception is responsible for image feature extraction. Imagenet dataset is used to train the CNN model called Xception. It includes the labeling of an image with English keywords with the help of datasets provided during model training.
#Caption generator generator
Image caption generator is a process of recognizing the context of an image and annotating it with relevant captions using deep learning, and computer vision. Now, let’s begin with a quick description of the image caption generator, CNN, and LSTM. Although we will discuss a brief about these techniques below if you want to learn them thoroughly you can click here. Basic knowledge of two techniques of Deep learning including LSTM(a type of Recurrent Neural Network) and Convolutional Neural Networks(CNN) is required for the same. In this guide, we are going to build one such annotation tool which is capable of generating very relevant captions for the image with the help of datasets. Even Caption generation is becoming a growing business in the world, and many data annotation firms are earning billions from this. But, what about computers? How can a machine process an image and label it with a highly relevant and accurate caption? It seemed quite impossible a few years back, but now with the enhancement of Computer Vision and Deep learning algorithms, availability of relevant datasets, and AI models, it becomes easier to build a relevant caption generator for an image. Whenever an image appears in front of us our brain is capable of annotating or labeling it.
This article was published as a part of the Data Science Blogathon
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