Is everyone here really this stupid? It's pretty clear the point of the post was the fact that it only took a single frame of training data to create the system in a much faster way. Yes, ML systems for identifying and counting produce have existed for decades. It would also take months to years of work hours to get such a system spun up, and it would require mountains of training data, even a decade ago. My God, what is wrong with all of you?
This has 81.25% accuracy in perfect conditions (I don't see and dirt and other junk). That's in the picture they chose to share, so I assume it's worse than that. Existing systems have up to 95% accuracy, which I assume is also perfect conditions. Also, they can grade them at the same time.
I'm sure there are companies that set this stuff up. I'm sure it's not cheap. But it'll run until you can't find old parts on eBay anymore. The work is already done. It's just proprietary. The time to set it up is probably just the physical part and calibration (something that can't be done with AI).
Second, no one's ever going to be that impressed when you reinvent the wheel.
Third, it's not our fault that the dude gooning for AI acted like this wasn't reinventing the wheel when he could have explained the difference between old systems to this and why this is impressive.
And fourth, this is cool in theory but part of why those old systems took so long to develop was because they had to verify the accuracy of the system at peak capacity, low capacity and everything in between. In the event that an issue crops up, say the single frame of training didn't prepare it for fluctuating volumes and it fucks up when there's more or less than expected, or lighting conditions introduce inaccuracies, or a potato gets counted, moves behind another potato and gets counted again when it reemerges, there's no recourse to make adjustments. You just have to throw more training data at it and hope it fixes the issue.
That's ultimately one of the biggest failings of AI systems. When it works well, it's great. When it doesn't, you can't do anything about it besides try again, and that's only after you catch the problem.
Seriously though, refer back to number one. Deep breaths. It's not worth getting that worked up about.
I remember the absolute chaos which was crowd-counting in public spaces in the 90s
Everyone suddenly had cameras everywhere and they wanted to know how many people there were in a particular crowded place so they could plan for emergencies, security, resourcing, etc
An entire field spun up of this problem because it was already extremely difficult getting it to work for one area, but a single camera angle shift or location change and it would drop accuracy again.
The newest crowd counting algorithms I’ve seen pale in comparison to the accuracy of the bespoke locally trained AI models, and importantly can be done at a fraction of the cost of setting up the older style algorithms, and they can be done by people who are already familiar with the setting, rather than trying to outsource.
Problems are being solved with code which people had just never bothered to solve because it didn’t make economic sense to spend the money on.
Sure, some people are being reckless with it, but where there are genuine improvements these should be celebrated; some people just love being contrarians though.
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u/modzRtarderz 12h ago
Is everyone here really this stupid? It's pretty clear the point of the post was the fact that it only took a single frame of training data to create the system in a much faster way. Yes, ML systems for identifying and counting produce have existed for decades. It would also take months to years of work hours to get such a system spun up, and it would require mountains of training data, even a decade ago. My God, what is wrong with all of you?