I just wanted to say that if you can count particles of things in microscope images, you can probably make great inroads at companies that do drug testing, like Covance or PPD in America. They're paying chemists to circle blobs on paper and count them. I bet they'd even appreciate someone to count their circles!
The company I work at did a project like this where the goal was counting of different types of bacterial colony forming units (CFUs) that were cultured in Petri dishes [0]. We presented our results at ESANN [1].
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.
Depends on the industry as well. Cell counting is a huge part of life science research and it's a well established business in lab-based pipelines (integral part of commercial solutions). However if you try to enter the realm of clinical field such as histology and pathology assistive technology it suddenly becomes tricky. Same with innovative diagnostic tools. You need clinical trials for those.
Yes, we should be able to count them if they are visible in the picture. Please email our support with a couple of sample images.
We are also doing classification for some of our templates/clients.
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.
For most of our clients, we get to 100% accuracy. For some more difficult scenarios, a lower accuracy is ok for estimations (e.g. estimating the crop in agriculture).
It's also super easy to correct mistakes or to add items that are not visible in the image.
You don't mean 100% accuracy, that means mistakes are impossible. Perhaps your error rate is very rare, but it's a bit concerning you don't attempt to quantify it.
I disagree. If we consider a single customer with a particular application, we probably imagine an iterative process like this: the customer supplies some sample images, the system gets a few wrong, they add special cases, fine-tune the hyperparameters, whatever, and after all this they literally get 100% on the customer's holdout data. If that happens for several customers then the GP statement is justified (no other number is possible for these customers).
Possibly you're thinking of a single error rate across all customers? For other customers, as stated, it's not 100%. But taking an average across multiple customers is not meaningful when some are counting pearls and some are counting crops.
Suppose it depends on which part of the chain he attributes error, plausible lighting or poor positioning - anything that is an employee duty - does throw it off and then they scrub it.
But yeah. Still.
The article shows a photo with the pearls highlighted with circles. I'm generally skeptical of AI, but this system produces a certificate that a human can verify much faster than doing the work themselves. IMO, that's the right way to do it. You can trust the system 99% of the time, and check a random 1% by eye every day. Or, you can keep a human fully in the loop and still save time (though, if the error rate is too low, humans will get bored)
We iterated through different ideas and numbers. It's not an easy task, I can tell you that.
We also try to have a good variety of choices: enterprise license, yearly, monthly or even packages of 24h.
And we also have some free demo counting template, you don't need a license while using them. Including one to help people involved in the vaccination process: https://countthingsqanda.com/?p=1587
It seems to me that it would be useful for the app to count items on the left and right of a ruler (or some line) so correct number of items can be separated out from a pool.
What approach does it use? Does it use image segmentation (e.g. U-net) followed by classic image analysis? Or does it get the counts directly from the network?
If you have the skills, you should definitely try it.
We have a full team working on this for about 7 years. Constant improvements to our algorithms, new challenges, new technologies. A ton of other functionalities besides counting (e.g. forms, reports, integration). A lot of work on the backend, UX. Also, a lot of sales and marketing involved.
It is impressive you do it so fast. In case you have not thinked about it, some people will be interested by it in life sciences. Best I could do with the picture available on your website https://imgur.com/a/FKEkiFz compared to https://countthingsqanda.com/wp-content/uploads/2021/01/5-17... I got some wrong counts but the picture is really tiny to work with.
Technically many could do what dropbox did to create a similiar product when they were part of yc class. Many HN have a personal dropbox setup they made themselves.
The key that allowed them to be a billion dollar business was marketing, hype, strategy all things that could apply to any product technical or not.
If you want to make something similiar you could probably get to 80% really quickly. Putting together a product with a reputation is a different event with timing luck and risk. Building that 80% is low risk but high personal reward.
Has anyone built something to read and identify palms or lines in the hands?
