djoser VS cvat

That's what I'd usually say too, but this issue does give some "neglected project" vibes - the version on pypi is still quite a bit older than what's in the repo.

e.g.: https://github.com/sunscrapers/djoser/blob/master/djoser/constants.py

Right now, I'm using dj-rest-auth for Token based authentication. I store token in the localStorage. Earlier I was using djoser for adding auth in the project (I wrote article about my approach with djoser).

View on GitHub

Djoser is an open-source authentication library for Django. It is a simple library for providing basic authentication in a Django app, and it is used alongside the Django REST Framework. In this article, we will be looking at how to customize URLs in Djoser.

For Djoser this looks to be fine, there is an issue mentioning Django 4.0 but it looks like that just requires a work around for a dependency.

djoser if you are using DRF

Customizing Serializers

A reusable library implementing auth logic in DRF: https://github.com/sunscrapers/djoser

I'm using Djoser on the backend and I want to save tokens in Redis when they are created. I want this because I want to single user from one account can use my app, so I'm about to store all created tokens and then invalidate them if a user with this `id` login's from another device/browser. So my question is, am I need to redefine this method? Please push me in the right direction, I'm kinda lost. Maybe there is a better solution.

Another powerful resource is CVAT, the Computer Vision Annotation Tool which supports both image and video annotations with advanced capabilities such as interpolation of shapes between frames, making it highly suitable for computer vision.

CVAT has an open source repo under MIT license: https://github.com/opencv/cvat I've not worked with it directly but it might be a good place to start.

ref https://github.com/opencv/cvat/issues/6061

an open source annotation tool that integrates object detectors is CVAT https://github.com/opencv/cvat however, using your own detector might require some coding. there is an integration for yolov5, but without modification it only loads the pretrained models.

This integration is currently available in the open-source version of Computer Vision Annotation Tool (http://github.com/opencv/cvat) and coming soon to CVAT.ai cloud (http://cvat.ai/)! Please use it for your computer vision projects to segment images faster.

You can download the CVAT docker from a github (Link) and install it yourself, keeping all data local. And here are two options - locally on your personal computer (or company server) or in your own cloud (there are instructions on how to do this with AWS).

In this release, CVAT introduced new features based on our vision and suggestions in the CVAT community, plus addressed more than 20+ reported bugs.

You can find more information here Waiting for your feedback here: Discord, LinkedIn, Gitter, GitHub