A dataset is a collection of image data that is used to build an AI model. In order to recognize content in videos and images, a neural network must learn how, for example, people or objects differ from each other. The AI learns this via so-called sample data, in the form of image data.
Creating and managing a dataset requires a great effort of organization and administration. In most cases, data sets are managed in different folder structures on internal servers and are very confusing. Using these data sets for training is also often very complicated for customers without administrative knowledge.
With DeepVA, we have developed a platform that, in addition to using an AI to analyze the image and video data, makes it possible to easily upload and manage large datasets on the underlying platform.
In these datasets, you can not only create classes but also enable, disable, merge or delete them. It can be easily determined within each class what should or should not be included in the training. Data sets, as well as image data, can be evaluated, i.e. it can be determined which image data is suitable for successful training and which is not. Faulty or poorly recognizable image data is left out of the equation.
Thanks to the fast and easy access to datasets, that are easily managed within the platform, companies can save a lot of time and effort while focusing on the core business. It only takes a few clicks to perform a training and the subsequent analysis.
faster data acquisition
DeepVA's approach to integrating AI into existing MAM-systems really excites me. This way, we can make artificial intelligence accessible to smaller media companies. Behind DeepVA is a small team that is results-oriented and professional, while remaining flexible and customer-focused. We enjoy working with this small, innovative start-up from southern Germany.
Integration of image and video recognition into a MAM
(media asset management) system.
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