Hi, there! I'm one of the project managers at CountThings, I'll try to respond to comments here but I guess you can AMA.
Probably a very naïve question but, how did you know/find out that an app to count things could be a profitable business?
A Japanese company cut 80% of the time needed to manually count pearls
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Re: A Japanese company cut 80% of the time needed to manually count pearls
#52Earlier quoted context omitted.
The old method used to be template matching, and it probably still works well enough for problems like this. There are almost certainly now better approaches possible using Machine Learning. In my experience, the main challenges with problems like this are dealing with varying lighting, scales, orientation and perspective. These can quickly become of diminishing returns – especially if the solution is provided as an…
My bet is they just invested a few months making a varied dataset, a few grand on mturk using labelme, some image augmentation, a few engineering tricks for a nice UX,some strategies for getting the model to converge on tricky test images, and got it to hit 100% accuracy fairly quickly.
We are working on this product in-house for 7 years now. We are an internal startup from a company that does custom computer vision and ML software.
No mturk, no tricks, a lot of work in ML and in UX as it is not a very straightforward task.
Re: A Japanese company cut 80% of the time needed to manually count pearls
#53For anyone who's complaining about the price, take a look at their whole offer:
Re: A Japanese company cut 80% of the time needed to manually count pearls
#54Hi, there! I'm one of the project managers at CountThings, I'll try to respond to comments here but I guess you can AMA.
Probably a very naïve question but, how did you know/find out that an app to count things could be a profitable business?
No, it's actually a pretty good question.
We are a custom ML & computer vision software company and about 7 years ago we got several requests in a short time period for counting items in images. We thought it was a great idea for a product and kind of started our journey as an internal startup.
Re: A Japanese company cut 80% of the time needed to manually count pearls
#55Call me naive, but wouldn't this be a lot easier - and more low-tech, thus cheaper, to sort by size with a sieve, then weight individual size-bags, and divide by the size of one pearl?
Assuming all pearls have comparative density.
Re: A Japanese company cut 80% of the time needed to manually count pearls
#56Call me naive, but wouldn't this be a lot easier - and more low-tech, thus cheaper, to sort by size with a sieve, then weight individual size-bags, and divide by the size of one pearl? Assuming all pearls have comparative density.
So you need to make / order sieves, then get the counting person to sort, bag, weigh the items, calculate results. This will have a lot of mistakes too. And you end up with a process working only with that type of pearls. (Edit: forgot the solution debugging time)
The alternative here is to point the phone at a table.
The labour costs are relatively high and dominate most business costs. $100/mth is a rounding error in comparison. If you saved 4h of median-us-wage of work a month, it's free.
Re: A Japanese company cut 80% of the time needed to manually count pearls
#57Is ML here needed? When I was working in a lab we used the opensource ImageJ for counting cells. It seems like it'd do a great job here too.
Re: A Japanese company cut 80% of the time needed to manually count pearls
#58Earlier quoted context omitted.
My bet is they just invested a few months making a varied dataset, a few grand on mturk using labelme, some image augmentation, a few engineering tricks for a nice UX,some strategies for getting the model to converge on tricky test images, and got it to hit 100% accuracy fairly quickly.
We are working on this product in-house for 7 years now. We are an internal startup from a company that does custom computer vision and ML software. No mturk, no tricks, a lot of work in ML and in UX as it is not a very straightforward task.
> It is not a very straightfoward task.
I'd well believe it. I used to write computer vision applications for semiconductor manufacturing equipment and there we were able to strictly control the distance from camera to object, lighting etc. and even still getting necessary reliability was not simple. When a failure could lead to damaging a whole wafer, i.e. hundreds of thousands of dollars, 99% accurate is not good enough.
Re: A Japanese company cut 80% of the time needed to manually count pearls
#59Hi, there! I'm one of the project managers at CountThings, I'll try to respond to comments here but I guess you can AMA.
How accurate is the counting? For examples like the case study, I presume your customers will want very close to 100% accuracy, but many other use cases will only need rough numbers. How do you calibrate / verify your app?
You have to take into account the error rate of human counting when making the comparison.
I can also imagine the new process have other benefits. Like before, if a customer complained of getting fewer than ordered pearls, they would probably have to acknowledge that as a counting error on their side but not knowing. Now they have a picture/data to refer back to.
At least that's how it is where I work; the automation leads to reduced errors, and when they happen the amount of data makes it easier to track where it went wrong.
Re: A Japanese company cut 80% of the time needed to manually count pearls
#60> All good apps have already been created. There is no room for innovation. > Hold my beer: > I made an app that counts. Honestly, the simplicity of the idea is amazing.
People often don't know what's really needed by businesses until they see the use case. I remember 2 decades ago a family friend making fun of his son's project where they identified bad apples on a processing line in a packing facility. Pretty close to this project. He saw it and still didn't get it.