cvat VS dvc

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

Collaboration and version control are crucial in AI/ML development projects due to the iterative nature of model development and the need for reproducibility. GitHub is the leading platform for source code management, allowing teams to collaborate on code, track issues, and manage project milestones. DVC (Data Version Control) complements Git by handling large data files, data sets, and machine learning models that Git can't manage effectively, enabling version control for the data and model files used in AI projects.

What you’re describing sounds like DVC (at a higher-ish—80%-solution level).

https://dvc.org/

See pachyderm too.

10. DVC by Iterative | Github | tutorial

Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.

I was using DVC (https://dvc.org/) for some time to help solve this but it was getting hard to manage the storage connections and I would run into cache issues a lot, but this solves it using git-lfs itself.

DVC (Data Version Control):

I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.