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yeonseok-zeticai

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On device AI with mobile hardware utilizations

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1121
πŸ‘‹ We're excited to share our project on mobile face detection using MediaPipe and ZETIC.MLange.

πŸ”‘ Key highlights:
1. Introduction to Mediapipe face detection model
2. Developing on-device AI applications with ZETIC.MLange
3. Guide to creating object detection apps utilizing Mobile NPUs

πŸ“± Learn how to build a high-performance face detection app that operates entirely on-device, no cloud required! We explore:
- Real-time face analysis techniques
- Enhanced security measures
- Privacy protection strategies

πŸŽ₯ Check out our demo video showcasing real-time face detection:
Face detection Demo: https://youtu.be/GXtJKk7MdjQ?si=41UFKL8IBwx5nlxs

πŸ“š Full Tutorial
For a step-by-step guide and in-depth discussion, read our full blog post:
https://zetic.ai/blog/implementing-face-detection-on-device-ai-with-zetic-mlange

We'd love to hear your thoughts, experiences, or questions in the comments below.
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1827
πŸš€ Revolutionary Method to Convert YOLOv8 to On-Device AI with mobile NPU utilizations

Attention AI developers and engineers!
Discover how ZETIC.MLange can effortlessly transform YOLOv8 models into on-device AI with mobile NPU utilizations. πŸŽ‰

πŸ’‘ We highlight the power of mobile NPU, showing how it outperforms CPU in processing speed. The results speak for themselvesβ€”NPU-driven execution is faster, smarter, and more efficient.
* Real-time demos are no easy feat but with ZETIC.MLange, we’ve made it possible!

πŸŽ₯ Watch the video to see how we’re revolutionizing on-device AI.
: https://youtu.be/LkP3JDTcVN8?si=6Ha5vHA-G7jZE9oq

🌟 Our team has successfully implemented YOLOv8 as on-device AI using ZETIC.MLange. This innovative approach enables high-performance object detection across various manufacturers mobile devices.

πŸ” Curious about the details? We've shared a comprehensive guide on our blog. Check it out through the link below!
πŸ“š Blog link: https://zetic.ai/blog/implementing-yolov8-on-device-ai-with-zetic-mlange

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