machinelearning-samples VS ML.NET

I kinda have to agree, just take a look at where Microsoft ML.NET is headed: https://github.com/dotnet/machinelearning-samples

I used Netron to inspect the onnx file and the output is named "output" and the type if float32[1,25200,6]. I was using a tutorial I found here but it's based on Tiny YoloV2 which is says has an output of "None x 125 x 13 x 13" so I don't know if I can apply the same logic here. In case it matters, my model only has a single label/class.

In the meantime, I recommend looking into Microsoft's documentation on ML.NET for additional details or checking out their ML.NET samples on GitHub

You can direct your question to https://github.com/dotnet/machinelearning/issues. Perhaps it is already documented.

Here I'm analyzing all projects in the src and test directories of the ML.NET repository. I chose to include these as separate paths because they represent two different groupings of projects in this repository.

Important Note: looping over repository commits takes a long time for large repositories. It took 52 minutes to analyze the ML.NET repository this code example refers to, which had 2,681 commits at the time of analysis on February 25th, 2023.

We checked the ML.NET 1.7.1 version. The source code of this project's version is available on GitHub.

machine learning? https://github.com/dotnet/machinelearning

You can follow some of our plans by taking a look at our roadmap which we'll be updating shortly to more accurately reflect the areas we're investing in.

Re: ONNX, if you run into similar issues in the future, feel free to reach out in our GitHub repo or the ONNX Runtime repo and we'd be happy to help!

Good clarification, I think using something like ML.NET could be cool but I have some experience with Blazor that might be fun to use as well, I think generally performance monitoring or optimizing systems seems interesting to me, and I'm really open to other ideas as well. Let me know if any of that helps narrow my question down!