make-sense VS cvat

Hi 👋🏻! I’m creator of https://makesense.ai. It supports Instance Segmentation. Take a look at the repo: https://github.com/SkalskiP/make-sense

I created tool called MakeSense: https://github.com/SkalskiP/make-sense it is completely free and open sourced on GH

Haven't heard of those two, but would be really awesome to see an integration. We have an open API[1] for just this reason: we really want to make it easy to use (and source) your data across all the different tools out there. We've recently launched integrations with other labeling[2] and AutoML[3] tools (and have integrations with the big-cloud AutoML tools as well[4]). We're hoping to have a bunch more integrations with other MLOps tools & platforms in 2023.

Re synthetic data specifically, we've written a couple of how-to guides for creating data from context augmentation[5], Unity Perception[6], and Stable Diffusion[7] & are talking to some others as well; it seems like a natural integration point (and someplace where we don't need to reinvent the wheel).

[1] https://docs.roboflow.com/rest-api

[2] https://github.com/SkalskiP/make-sense/pull/298

[3] https://github.com/ultralytics/yolov5/discussions/10425

[4] https://docs.roboflow.com/train/pro-third-party-training-int...

[5] https://blog.roboflow.com/how-to-create-a-synthetic-dataset-...

[6] https://blog.roboflow.com/unity-perception-synthetic-dataset...

[7] https://blog.roboflow.com/synthetic-data-with-stable-diffusi...

Two things that carried me the most are my blog https://medium.com/@skalskip - which gave me my first job in computer vision, and my open-source GitHub project: https://github.com/SkalskiP/make-sense - which gave me all my jobs since I created it.

Link to GitHub project: https://github.com/SkalskiP/make-sense

I have also looked into make-sense and currently the docker and the npm refuse to work. I have already opened a ticket describing the issue .

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