open_model_zoo VS Dlib

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

dlib

The apparent gender estimates from photos are using dlib, and I really ought to get what I'm doing cleaned up in such a way that other people can use it easily.

I really like dlib's code https://github.com/davisking/dlib

Additionally, C++ may be used for extremely high levels of optimization even for cloud-based ML. Dlib and Kaldi are C++ libraries used as dependencies in Python codebases for computer vision and audio processing, for example. So if your application requires you to customize any functions similar to those libraries, then you'll need C++ knowhow.

If you know C++, you don't need anything else. Go and learn APIs for C++ libraries. If you're into DSP, why not study Dlib?.

The data is mostly in this spreadsheet. The apparently facial gender estimates are made with Dlib. The mental health assessments are from Beck's Depression Inventory and the Snaith-Hamilton Pleasure Scale. The graph is made with gnuplot.

The plugin uses dlib library with a very fast HOG detector for both face recognition and detector following the relative examples.

The dlib facial recognition model thinks that I am now a distance of about 0.3 from where I started, which is far enough to start getting many false positive matches, but still within the design intent that different pictures of the same individual will be within 0.6 of each other.

Dlib's face recognition module thinks that I am about 0.25 units away from where I started; its design intent is that distinct individuals will be 0.6 or more apart, although in practice other people start showing up around 0.3.