Content video and photo AI system
A project task
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.
Stages of work:
  1. Find ready-made solutions
  2. Customization to Customer Data
  3. Implementation of the algorithm
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 - инструмент, в котором есть все
необходимые библиотеки и программы для работы нейросети. Благодаря ему, нейросеть можно легко и просто запустить на любом компьютере.
A framework for creating web applications, thanks to which the logic of interaction with a neural network via the Internet is configured.
Project team

Andrey A.
Dmitry I.
Frank S.
Areas of use of the service

The project is useful in any area where the identification of a person by 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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