developer-roadmap VS mediapipe

Developer Roadmaps - roadmap.sh

Learning the basics of web development opens doors to many other careers in the tech field.  Roadmap.sh provides guides, learning paths, and roadmaps to point developers in a direction of their choosing.

There's a website I personally follow specific for roadmaps called as “roadmap.sh” where all the roadmaps are available. You can check it out, if you like. Here's the link: ▶️ https://roadmap.sh/

If you're starting your journey in web development, here is a roadmap to follow. Understand that Web development is not merely an extension of programming; it's a distinct field that requires a unique blend of coding and visual design skills. Embrace the importance of visual aesthetics from the get-go, and continuously work on improving your design sense. Trust me it will pay off in the long run, and you'll be able to create truly exceptional web experiences that not only function well but also look and feel amazing.

Github developer roadmaps (backend, frontend, cloud ...)

Roadmap.sh (https://roadmap.sh/)

Web Developer-Roadmap GitHub Link: developer-roadmap Crafted by kamranahmedse, this roadmap acts as a compass for developers navigating the vast landscape of technologies. Covering front-end, back-end, and DevOps, it aids developers in charting a learning path aligned with their goals.

✅ kamranahmedse/developer-roadmap: https://github.com/kamranahmedse/developer-roadmap

It may also be beneficial to start with a high-level overview of what there is to learn in a given area, to understand the overall lay of the land - and then use ChatGPT to dig deeper into selected topics. There are many good resources that provide such an overview, like roadmap.sh or my "Definitive Guide to Succeeding as a Professional Dev".

mediapipe/docs/solutions/pose.md at master · google/mediapipe · GitHub

Today, we announce MediaPipe diffusion plugins, which enable controllable text-to-image generation to be run on-device. Expanding upon our prior work on GPU inference for on-device large generative models, we introduce new low-cost solutions for controllable text-to-image generation that can be plugged into existing diffusion models and their Low-Rank Adaptation (LoRA) variants.

You can just grab the TFLite version! https://github.com/google/mediapipe/blob/master/docs/solutions/models.md

I believe Google already released transformers under an apache 2 license with a patent grant:

https://github.com/google/mediapipe/blob/master/mediapipe/mo...

I was going to say that I like the MediaPipe Selfie Segmentation model for doing this sort of thing in a web page, but I've just noticed (when getting the GitHub link[1]) that Google have marked the code as legacy[2] ... no idea if the new solution is better/easier to use[3].

For what it's worth, my CodePen using the old model is here: https://codepen.io/kaliedarik/pen/PopBxBM

[1] - https://github.com/google/mediapipe/blob/master/docs/solutio...

[2] - "Attention: Thank you for your interest in MediaPipe Solutions. As of April 4, 2023, this solution was upgraded to a new MediaPipe Solution."

[3] - https://developers.google.com/mediapipe/solutions/vision/ima...

I have used mediapipe very successfully in multiple projects and it's very easy to get running. You can choose from many different vision tasks including hand landmarks ( https://github.com/google/mediapipe/blob/master/docs/solutions/hands.md )

I found MediaPipe's Face Mesh and was impressed with how simple it was to get going, but it just gives you the landmark points and I've not gone any further yet.

We've trained ControlNet on a subset of the LAION-Face dataset using modified output from MediaPipe's face mesh annotator to provide a new level of control when generating images of faces.

Medipaipe library link: https://mediapipe.dev/