make-sense VS roboflow-100-benchmark

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 .

Thanks for sharing @jonbaer! I’m one of the co-founders of Roboflow. Some additional resources and context:

* Blog Post: https://blog.roboflow.com/roboflow-100/

* Paper: https://arxiv.org/abs/2211.13523

* Github: https://github.com/roboflow-ai/roboflow-100-benchmark

At Roboflow, we've seen users fine-tune hundreds of thousands of computer vision models on custom datasets.

We observed that there's a huge disconnect between the types of tasks people are actually trying to perform in the wild and the types of datasets researchers are benchmarking their models on.

Datasets like MS COCO (with hundreds of thousands of images of common objects) are often used in research to compare models' performance, but then those models are used to find galaxies, look at microscope images, or detect manufacturing defects in the wild (often trained on small datasets containing only a few hundred examples). This leads to big discrepancies in models' stated and real-world performance.