Major Image Recognition And Annotation Trends

Image recognition and annotation technologies are evolving. New techniques that allow you to solve a wide variety of tasks quickly appear. We are happy to present five major trends in image recognition and annotation.

Image recognition and annotation technologies are evolving. New techniques that allow you to solve a wide variety of tasks quickly appear. We are happy to present five major trends in image recognition and annotation.

Active learning for machine learning

Most supervised machine learning models require huge amounts of data to be trained with good results. And most companies have difficulties with providing data scientists with this data, in particular labeled one.In most cases, data scientists are provided with a big, unlabelled data sets and are asked to train well-performing models with them. In most cases, the amount of data is too big to be manually labeled, and it is quite difficult to train good supervised models with this data.Active learning is the process of prioritising the data which needs to be labeled to have the highest impact on training a supervised model. Active learning can be used in situations where the amount of data is too large to be labeled and some priority needs to be made to label the data in a smart way.

Edge computing

Edge computing is a computing model in which computing takes place near the physical location where data is being collected and analyzed, rather than on a centralized server or in the cloud. This new infrastructure involves sensors to collect data and edge servers to securely process data in real-time on-site, while also connecting other devices, such as laptops and smartphones, to the network.Cloud computing and the internet of things (IoT) have elevated the role of edge devices, ushering in the need for more intelligence, computing power, and advanced services at the network edge. This concept, where processes are decentralized and occur in a more logical physical location, is referred to as edge computing.

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