Content video and photo AI system

A system of automatic classification of incoming photo and video content is required for subsequent sorting of materials. The system will be used for the facial search service.

Collecting and processing a large amount of data is a complexity that has been overcome. A ready-made algorithm for finding faces through vector representations does the job quickly and well.

A resource for demonstrating the visual component after training a neural network. Thanks to Streamlit, the customer can test the neural network before launching the service. So the product can be improved if there are bad results.

Converts requests received from Nginx into a format that the web application can process, and also runs code when necessary.

Elasticsearch is a database with a built-in ANN algorithm not found in other databases.

ANN (Approximate Nearest Neighbor) is an algorithm that quickly and efficiently finds a huge number of examples.

Downloads and launches the necessary services from the Internet. It can process them very quickly and is usually configured to start only those services that the web application really needs.

Thanks to this function, the model learns to separate figures in such a way as to find the right person among similar ones.

Docker is a tool that has all the necessary libraries and programs for the operation of the neural network. Thanks to him, the neural network can be easily and easily launched on any computer.

A framework for creating web applications, thanks to which the logic of interaction with a neural network via the Internet is configured.

Frank Sh.

The project is useful in any area where the identification of a person by a photograph is required. This technology was used in one of our projects, where it was possible to search by photo the names of actors and films with their participation.

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