StackML is a web-based visual tool to create & train custom deep learning models which can be downloaded in Core ML or Tensorflow format to integrate directly in apps, or used as an API.
Hello @neetusingh1791, do you have any link with https://scale.ai? Your website really look the same. I hope it's not a simple copy/paste of their design...
Hi everyone,
StackML is a platform to use machine learning models in-browser.
Reasons we built this tool:
There are different type of makers who want to use machine learning in their apps. But face lots of hurdles like infrastructure setup, lack of AI expertise, server cost, etc.
We want to help creators of all kinds to start building and using machine learning models in their apps in the simplest way.
With StackML they require zero coding, no AI expertise, no server cost as models re built in-browser. With StackML javascript library people can use state of the art machine learning models in their apps with few lines of code. Also, StackML is completely free.
Would love to hear your thoughts!
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@neetusingh1791 Hi Neetu, Just a quick question can we use StackML to detect face and then create face filters like snapchat?
@neetusingh1791, your product is a direct and blatant copy of Runway (https://runwayapp.ai/), where you are a registered user in the private beta by the way. You even copied the same text from the tutorials (example reference: https://imgur.com/a/ICMNC9K)! This violates Product Hunt's Terms of Service.
It's great having more people joining the discussion about enabling ML for creators, but perhaps instead of lifting the entire UI and documentation from another team, consider how you can engage with the creative & accessible AI community in a more productive way.
Hey @c_valenzuelab,
StackML is a (web-app) to train & use ML models in-browser, whereas Runway is a (desktop application) which runs with the help of docker. I don't see any similarity here. We just keep a tap of all the products in our space & get inspiration.
Also, as I checked I got beta access for Runway just a week ago.
@aneesh_rayancha Yes, you can use it using one of our pre-trained model Face Landmark with the help of StackML library. Let me know if you have any other queries. You can email me at neetuk@stackml.com.
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Hi @neetusingh1791,
Came across this exciting web-app. Just had a quick question, is there a phase where we can test the features provided by the StackML javascript library, for the compatibility with our native application, before actually integrating them?
@ranjan4816 You can test the pre-trained as well as custom models in the dashboard itself, before using it in your application.
Also, if you can share, I am interested to know about your use case.
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@kkkosariya So the use case of our app is to basically provide access to only authorised personals for sign-in/sign-out to our work-space, based on face detection.
This looks really cool. My use case is that I need to train a model to identify and classify logos (in a different way than the Google/Azure/AWS ML APIs support). Does StackML support this type of use case?
@tela Yes you can. Reach out to me directly if you have any query kamalk[at]stackml.com.
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This is pretty exciting! The "WRIST" demo was a flop because it was confusing to understand (poorly written instructions + it would be helpful to provide screenshot examples of what you want the visitor to do)
Thanks, @kevin for the hunt.
StackML is in early access, but you can sign-in without any waiting for the next few days.
Of course, this is just the start, we have a ton of new features coming up.
We would love to have your feedback, reach out at kamalk@stackml.com.
Or if you find any bug, please report at support@stackml.com.
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Like the idea as a concept, but don' tlike the fact that it's not as asynchronous as I wish it was.
Pros:
Machine learning in the browser.
Cons:
The browser completely freezes while the model is executing. This can't be used for anything serious, as nobody wants a frozen UI.
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Like the idea as a concept, but don' tlike the fact that it's not as asynchronous as I wish it was.
Pros:Machine learning in the browser.
Cons:The browser completely freezes while the model is executing. This can't be used for anything serious, as nobody wants a frozen UI.