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Telecom companies are facing quality issues with the deployment of #fibre infrastructure. We teamed up with Pulse to hear how telecom executives are currently dealing with this challenge. D iscover their views. hubs.li/H0FwQXD0

[SAVE THE DATE] MEETUP COMPUTER VISION Hello everyone! The first Computer Vision MeetUp of 2021 will take place **next week**, on Wednesday, January 13th at 7pm online. Registration here: bit.ly/meetupcv16

That's it, this thread is over. You now know the basics of image recognition. Thank you very much for reading it and if you want to know more, some documents are available here 👇 and in DM.

I see you coming. Why not always use the most precise technique? Very good question, it's nice to see you follow! Quite simply, the higher the accuracy, the higher the cost and the longer it takes to set up your recognition system.

This meticulous analysis of each component of an image is mainly used for projects that are both complex and delicate, which cannot be satisfied with approximate precision.

The third technique is somewhat different. Called Detection, it consists in making a bounding box appear on the image in order to locate the element(s) you are looking for.

The second technique is called tagging, and it is nothing more than an evolution of classification. The process is the same, only instead of assigning a class to the image, tagging puts several labels on it, as you can see below.

The easiest task, but also the more general one , is classification. Its objective is simple: to assign a "class", i.e. the AI determines the category to which an image belongs, as in the image below

Now it's getting interesting, right?