open_model_zoo VS Face Recognition

Search engines and chatbots are rapidly advancing with the introduction of ChatGPT, but search engines are still running on models like BERT (Bidirectional Encoder Representations from Transformers) for question-answer functionality. An interesting OpenVINO jupyter notebook #213 demonstrating question-answer functionality with a SQuAD v1.1 training set trained BERT model and transformer functions using either an embedded paragraph or a link to a website. I find it still really compelling to see how this works, basically how our widely used search engines function though on a much bigger scale.

If not, below a github link https://github.com/openvinotoolkit/open_model_zoo/tree/master/demos/common/python

Camera connected to a PI? Something like this could run locally: https://github.com/ageitgey/face_recognition

Have you tried this repo: github

One of the most common challenges is the black-box problem, when the pipeline becomes too complex to understand it would happen. This can make it difficult to identify issues with the system or to understand why it isn't working as we expected or make accurate predictions that saiwa company find out the solution for Face Recognition. Another challenge is the time required for organizations to deploy a machine learning model, which is increasing and make real-time computing difficult . To overcome these challenges, it's important to have an efficient and rigorous ML pipeline . ML level 0 involves a manual process with its own set of challenges, while ML level 1 involves ML pipeline automation and additional components . A well-defined machine learning pipeline can help to abstract the complex process into a series of steps, allowing each team to work independently on specific tasks such as data collection, data preparation, model training, model evaluation, and model deployment.

Second link is an easy to implement python library is you want to build it yourself https://github.com/ageitgey/face_recognition

It is similar to https://github.com/ageitgey/face_recognition, except that Ageitgey's cli only compares the first face found in the image to the first one found the the second.

You'd have alot more luck with something like DLIB or an open source implementation such as: https://github.com/ageitgey/face_recognition

To know more about face_recognition module https://github.com/ageitgey/face_recognition