Visual Recognition Tutorial with IBM Watson and Python

In the era of digital cameras, with people sharing billions of photos and videos daily, it’s impossible to manage the content manually. Therefore the necessity of automatizing visual recognition has rapidly increased, proportionally with social networks. Image recognition can be used in numerous tasks, such as filtering out explicit content, labeling the content of images with meta-tags and performing image content search. Beyond social media, visual recognition can be used in various other domains: health, to trace visible symptoms on patients and contribute to the diagnostic, environment, to detect problematic areas, road traffic, to avoid accidents and build self-driving cars, and many others.

But while image recognition seems like a simple task for humans, a computer requires deep machine learning to perform this task. IBM Watson, a leader in cognitive computing, offers visual recognition service as API and uses deep learning algorithms to analyze images to give insights into visual content.

In this article, you will learn how to use Watson’s default classifiers for image recognition and create and manage custom classifiers which can analyze images according to the requirements of your application.

Read more about it here.

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