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Simple project to recognize hands in realtime. 👋 Serves as an Example for building your own object recognizer.
| Date | Stars |
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| 2026-07-24 | 346 |
| 2026-07-25 | 346 |
| 2026-07-28 | 346 |
| 2026-07-30 | 346 |
| 2026-08-06 | 346 |
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# Hand Gesture Recognition This simple sample project recognizes hands in realtime. 👋 It serves as a basic example for recognizing your own objects. Suitable for AR 🤓. Written for the tutorial [“Create your own Object Recognizer”](https://medium.com/p/7f8c09b461a1).  [Demo Video - on Youtube](https://youtu.be/P3Q8awgT9Lk) Tech: iOS 11, ARKit, CoreML, iPhone 7 plus, Xcode 9.1, Swift 4.0 ## Notes: This demonstrates basic Object Recognition (for spread hand 🖐, fist 👊, and no hands ❎). It serves as a building block for object detection, localization, gesture-recognition, and hand tracking. Disclaimer: The sample model provided here was captured in 1 hour and is biased to one human hand 👋🏼. It’s intended as a placeholder for your own models. (See [Tutorial](https://medium.com/p/7f8c09b461a1)) --- ## Steps Taken (Overview) _Here’s an overview of the steps taken. (You can also view my commit history to see steps involved.)_ 1. Build an Intuition by playing with Google CL's [Teachable Machine](https://teachablemachine.withgoogle.com/). 2. Build dataset. 3. Create a Core ML Model using Microsoft's [CustomVision.ai](https://www.customvision.ai/). 4. Run the model in realtime with ARKit. [Full Tutorial here](https://medium.com/p/7f8c09b461a1) _P.S. A few well selected images are sufficient for CustomVision.ai . For the sample model here, I did 3 rounds of data collection (adding 63, 38, 21 images per round). Alternating classes during data collection also appeared to work better than gathering all the class images at once._  ## License MIT Open Source License. 🧞 Use as you wish. Have fun! 😁
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:cabd89a6c06b8441, topic:computer-vision, readme:object detection