DeepLabCut VS lightweight-human-pose-estimation.pytorch

Use DeepLabCut, I also strongly suggest that you should fund their work: https://github.com/DeepLabCut/DeepLabCut

DeepLabCut is also worth mentioning here

1 - Detecting the dog's body parts was the most difficult portion of this, and thankfully I stumbled upon DeepLabCut (https://github.com/DeepLabCut/DeepLabCut) which enables training a model to track a specific animal(s) posture. In the video, this is basically the dots that are overlayed on top of the dog, and follower her around. DeepLabCut is basically just saying that this is where it thinks it recognizes "spine" and "tail".

I used https://github.com/DeepLabCut/DeepLabCut for the core dog tracking capability, then I wrote the code that analyzes the posture (output by the model, trained via DeepLabCut) of the dog.

I built a dog poop detector for my backyard using DeepLabCut (https://github.com/DeepLabCut/DeepLabCut) and some janky poop detection heuristics I wrote that processed on the detected posture of my dog, if it's in the frame of my security camera. If it detects my dog pooping, it will record the location in a CSV and draw all the locations on an up to date image of my backyard.

Also, check out DeepLabCut. My project wouldn't have been possible without it, and it's really cool: https://github.com/DeepLabCut/DeepLabCut

Well you could try DeepLabCut - https://github.com/DeepLabCut/DeepLabCut

Lightweight OpenPose: Runs in realtime >20fps confirmed, training code is provided

Using https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch as an example (or another repo, that was just one I found), could someone please give me a step by step process of how they read a repo for a research paper?