Hi - does anyone have any thoughts on why they used Yolo to perform the detailed analysis of the HD imagery instead of using Akida?
I get that there is a benefit in performing the initial triage of the incoming data using Akida to analyse low def images to ditch images without any ships present.
But why not train Akida to identify various types of ships and then use that to analyse the HD imagey?Instead they train Yolo to recognise different types of ships; my initial assumption is because an SNN is not able to perform that task?
My understanding is that I could take my CNN's (i.e. a trained CNN Yolo to recognise different ships) and convert that to an SNN to use with Akida and consume less power than if I was using a CNN [using this to convert my CNN https://doc.brainchipinc.com/user_guide/cnn2snn.html].
Is the SNN unable to perform the inferance on the HD imagery in granular detail, i.e. it can recognise a ship but not what type of ship because .........
- maybe the Akida hardware (ver 1) is not powerful enough to run inferance on HD images (is Akida 2 powerful enough)?
- we don't have the algorithms yet to recognise such detail (so our SNNtoCNN is not great and needs to improve)?
- I don't understand SNN's well enough and this tech is not capable of doing this level of detail?
I have used Yolo a lot on projects identifying different fish species, people in crowded events, etc and always assumed one day I will do this at the edge with an SNN (which is why I invested in Brainship) - now I wonder what I have missed?
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Hi - does anyone have any thoughts on why they used Yolo to...
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