“AI is one of the most important things humanity is working on. It is more profound than, I dunno, electricity or fire” — Sundar Pichai, CEO Google “I visualise a time when we will be to robots what dogs are to humans, and I’m rooting for the machines.” — Claude Shannon “There is no reason and no way that a human mind can keep up with an artificial intelligence machine by 2035.” — Gray Scott Enough has been said about the fact that we are moving towards an age of AI; it could be a blessing or an evil. Nobody knows; but what we know is that it will be thrilling for sure! AI is a considerably massive field. Today we will try to understand an application of which is a kind of , which is in turn a kind of Artificial Intelligence(just look at the picture below) Deep Learning ; Machine Learning In recent years, with the extensive on-going research, generation of massive data sets and availability of massive computing power, Deep Learning has become one of most exciting fields of this era. Lets have a look at one of the foremost and supreme applications of Deep Learning which at the forefront of innovation and technology. Image Recognition and Object Detection Image Recognition is at the sweet intersection b/w Deep Learning and . Computer Vision I have seen a lot of people using these two terms interchangeably. Well, its not the same thing. Lets see what the difference is! :) (also called Image Classification) Image Recognition This is the process of taking an image as input and outputting a class label out of a set of classes. Input — An Image Output — A class label to which the image belongs For instance, we have 3 class labels — { Lion, Lion, Laptop, Pen, Phone} Now if we give an image to the algorithm, it will tell if that image is a Lion, a Lion, a Laptop or Nothing. Input - Laptop Output Image Classification with Localization This process takes an image as input, outputs a class label and also draws a bounding box around the object to locate it in the image Input — An Image Output — A class label A bounding box + Example-: Input Output As you can see, with localization we also know the location of object within the image. Object Detection In this process, has to be applied on all the objects in the Image, which results in multiple bounding boxes Image localization Input — An Image Output — Class labels + Bounding boxes for all the objects These objects could belong to different classes(obviously it will only determine the classes on which the model is trained) Example -: Input Output This is how a typical result of object detection looks like. You can see that it has identified a , and a along with the . Pen Phone Laptop Now we know the difference between Image Recognition, Image Localization and Object Detection, lets take a look at the applications :) Applications One of the application of Object Detection is — undoubtedly one of the hottest innovation of the century. Self Driving Cars Some other applications are -: Face Detection Pedestrian detection Automating Checkouts in stores like Amazon Go So where do we go next? Now we know what these techniques are, next we can look at how can we build a simple model for Object Detection. Stay Tuned! If you like and understood the article, hit the clap icon 👏.

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<Micro Learnings> Image Recognition Vs Object Detection — The Difference

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<Micro Learnings> Image Recognition Vs Object Detection — The Difference

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Vipul Jain

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