sod VS Dlib

PixLab (https://pixlab.io) & FACEIO (https://faceio.net) | Full-or-part-time | Remote | Computer Vision / Full stack Engineers |

PixLab, a leading provider of Machine Vision, Face Recognition & Media Processing APIs is looking for:

* Embedded C & Computer Vision engineer(s) to work on the SOD (https://sod.pixlab.io), embedded computer vision library.

* Senior Python engineer with proficiency in PyTorch to work on FACEIO (https://faceio.net), our facial authentication web framework for web sites & apps.

* C++ developer with ML expertise to work on the port of Tiny-Dream (https://pixlab.io/tiny-dream), our embedded Stable Diffusion C++ library from ncnn to ggml.

* React/Vue JS Web developer(s) with expertise in fabric.js to work on a brand new, web based photo editing software backed by generative AI.

Reach out to Vincent via contact AT pixlab.io with your resume if interested.

Shameless plug: take a look to our embedded computer vision library SOD: https://sod.pixlab.io.

Khalid Elborai elaborates on their experiences integrating the SOD library into a Raku module.

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.