real-world use: which workflow works best for Computer Vision?

For real-world use: which workflow works best for Computer Vision?, I would separate the problem into setup, output quality and repeatability. real-world and computer fits into that because it can change the result quite a lot depending on the workflow. The original point — "I have been exploring Computer Vision and wanted to ask people who have tried it in practice. I am particularly interested in real-world use and future improvements. I would like…" — gives a useful starting point for that comparison.

One thing worth trying with real-world use: which workflow works best for Computer Vision? is a controlled comparison around real-world and computer. Use the same input for each test and note what changes in the output rather than judging from a single run. It also gives you a cleaner baseline if you want to compare tools or settings later.

For real-world use: which workflow works best for Computer Vision?, I would separate the problem into setup, output quality and repeatability. real-world and computer fits into that because it can change the result quite a lot depending on the workflow. It also gives you a cleaner baseline if you want to compare tools or settings later.

I think real-world and computer is the key part of this topic. A practical approach is to define the outcome you want, test it with a small example, and then check quality, speed and reliability separately. That way you can improve the workflow without losing track of what caused the improvement.