Hello everyone 
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I am excited to discuss the latest breakthroughs in object detection, especial;y those driven by Ultralytic’s recent innovations. How have the new features of YOLO models reshaped your workflow? What challenges have you encountered in applying these models to real-world scenarios?
Are there emerging techniques or tools that you believe will significantl;y impact the future of object detection? I am eager to hear about your insights and predictions.
Let’s share our knowl;edge and collectively shape the future of this exciting field.
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@faelannm welcome to the forums! I like the question you’ve raised, although I’m not sure that I can provide a lot of insight personally with regards to how things will improve, but I can speculate on how things will evolve from an application standpoint.
I’d guess that embedded models will become quite popular. Basically an out-of-the-box device with an embedded model that’s ready to go (which is happening now, but I think it’ll be more common). Somewhat like everything getting a screen or internet connectivity (like a toaster that connects to the cloud), many more devices will end up having models embedded in them from the manufacturer.
I also suspect that we might start to see model structures that are more built to use in specific or narrow use cases. Such as a model that is less general purpose and more specialized for detecting certain features of an object. As an example, I’m guessing that the structure of a model for defect detection in silicon wafer fabrication would be created differently and specifically, than the one used for detecting people.
What about you @faelannm any predictions or thoughts on where you think things will go?
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