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Sunday, June 30, 2019

Building a Computer Vision Model

A simplified, straightforward tutoral on a computer vision model.   This is the place you can get something impressive out of neural nets,  and an intro to the general AI method along them way.       Of most use too, pointers to existing databases to get started with.  We used ImageNet and WordNet tags, for example.

From KDNuggets:

How can we build a computer vision model using CNNs? What are existing datasets? And what are approaches to train the model? This article provides an answer to these essential questions when trying to understand the most important concepts of computer vision.  

By Javier Couto, Tryolabs.

Computer vision is one of the hottest subfields of machine learning, given its wide variety of applications and tremendous potential. Its goal: to replicate the powerful capacities of human vision. But how is this achieved with algorithms?

Let's have a loot at the most important datasets and approaches.

Existing datasets
Computer vision algorithms are no magic. They need data to work, and they can only be as good as the data you feed in. These are different sources to collect the right data, depending on the task:

One of the most voluminous and well known dataset is ImageNet, a readily-available dataset of 14 million images manually annotated using WordNet concepts. Within the global dataset, 1 million images contain bounding box annotations.  .... "

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