Fritz AI

@fritzlabs

Fritz AI is the machine learning platform for iOS and Android developers—teach your apps to see, hear, sense, and think.

Boston, MA
Vrijeme pridruživanja: studeni 2017.

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  1. Prikvačeni tweet
    12. pro 2019.

    Have an idea for a project, but don’t have enough data? With the new Fritz AI Dataset Generator, you can create ready-to-train datasets of 1000s of images with accurately-labeled keypoints—in minutes. Request early access ➡️

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  2. prije 4 sata

    With Fritz AI, mobile devs can now easily build apps powered by . No PhD required. 🛠Build apps, not models 👓Implement pre-built vision features 📱Easy SDK integration for & Test-drive features, read tutorials, and more:

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  3. prije 9 sati

    How powerful is our Dataset Generator? The video below shows a finger tracking model trained entirely with a programmatically generated dataset using just 12 seed images. Request early access: "

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  4. prije 10 sati

    In his latest for Heartbeat, implements a permanent storage system on with an database.

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  5. 31. sij

    Object detection is a task that identifies & locates objects within an image or video. But there's a lot more to know, which is why we created a comprehensive guide that covers: What object detection is, how you can use it, & how it works.

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  6. 31. sij

    Building models ready for deployment is just the start. Managing and optimizing those models is tricky. Luckily we've curated some incredible resources to help you create more efficient ML models ready for iOS.

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  7. 31. sij

    In the final post in his series on building an chat application from scratch, contributor show how to update and fetch new messages within active conversations.

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  8. 30. sij

    Use Style Transfer to bring real-time, artistic styles to your apps. ✅ Works on iOS and Android ✅ Use a built-in style or train your own ✅ Runs on-device, in real-time Start building today with a free developer account:

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  9. proslijedio/la je Tweet
    30. sij
    Prikaži ovu nit
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  10. 30. sij

    Contributor with another really interesting use case for on —predicting air pollution levels with a random forest regressor.

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  11. 30. sij

    For mobile devs, the thought of working with can be intimidating. We built Fritz so you can focus on building ML-powered apps, not models. Explore our and SDKs, pre-built vision features, tutorials, demo projects, and more:

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  12. 29. sij

    The world moves quickly. It's nearly impossible to keep up with all the latest amazing research efforts. But we're going to help you try! A helpful list of Heartbeat research guides for machine and .

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  13. 29. sij

    Excited to announce the Fritz AI Newsletter! Our new resource covering mobile machine learning, with expert takes on its opportunities and challenges, Fritz AI platform updates, and much more. Learn more and subscribe to stay in the loop:

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  14. 29. sij

    The very last volume of the Heartbeat Newsletter is live. Stay tuned for what's next. But until then, a peek at vol. 86: An AR-powered social network, LG’s edge AI play, Tinder’s new AI anti-harassment tool, on-device speech recognition, and more.

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  15. 29. sij

    Contributor with an excellent primer on implementing notification systems on .

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  16. 29. sij

    With our Dataset Generator, train a custom finger tracking model in 4 steps: 1) Get 12 pictures of hands 2) Generate 5k images & annotations 3) Train a mobile-optimized model w/ our notebooks 4) Integrate w/ our training template & SDK Request access:

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  17. 28. sij

    Create cinematic videos and gifs with Sky Segmentation. ✅ Works on iOS and Android ✅ Separates foreground from sky ✅ Runs on-device, in real-time Start building today with a free developer account:

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  18. 28. sij

    When implementing a notification system in , it's essential to build an intuitive, well-designed UI. In part 12 of his AirBnB clone series, contributor Kris Kaewsanmuang shows how to do just that.

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  19. 28. sij

    Image recogniton (aka image classification) is a foundational computer vision task, made easier & more powerful with . Our comprehensive guide covers (almost) everything you need to know: What it does, how it can be used, & how it works.

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  20. 28. sij

    In part 4 of his series on feature selection for , contributor explores embedded methods—techniques that include feature selection strategies within model training.

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  21. 27. sij

    The possibilities of embedding features on are truly limitless. Explore a taste of what's possible with our collection of Heartbeat articles and tutorials that will help you start building ML-powered iOS apps.

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