12
Ovi
๐ฅ
Generate Hollywood Style Actors on your Local Machine
Doron Adler PRO
Norod78
AI & ML interests
Fooling around with Generative machine learning models.
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SOTA OCR on-device with Core ML and dots.ocr
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There is no such thing as a tokenizer-free lunch
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๐ฏ RetinaFace On-Device Deployment Study: NPU Acceleration Breakthrough!
(Check details at :https://mlange.zetic.ai/p/Steve/RetinaFace)
TL;DR: Successfully deployed RetinaFace with ZETIC.MLange achieving 1.43ms inference on mobile NPU!
๐ Complete Performance Analysis:
Latency Comparison:
- NPU: 1.43ms (Winner! ๐)
- GPU: 3.75ms
- CPU: 21.42ms
Accuracy Metrics - SNR:
- FP16: 56.98 dB
- Integer Quantized: 48.03 dB
(Precision-Performance: Excellent trade-off maintained)
Memory Footprint:
- Model Size: 2.00 MB (highly compressed)
- Runtime Memory: 14.58 MB peak
- Deployment Ready: โ Production optimized
๐ Technical Implementation:
(Runnable with Copy & Paste at the MLange link!)
๐ Device Compatibility Matrix:
Tested on 50+ devices including Samsung Galaxy series, Google Pixel lineup, and Xiaomi devices, iPhones and iPads.
Consistent sub-5ms performance across the board!
๐ Applications Unlocked:
- Real-time AR/VR face tracking
- Privacy-preserving edge authentication
- Live video processing pipelines
- Mobile security applications
- Interactive camera filters
The democratization of high-performance computer vision on mobile devices is happening NOW! This study proves that complex CV models can run efficiently on consumer hardware without compromising accuracy.
Want to reproduce these results? Check out the benchmark methodology and implementation guide!
(Check details at :https://mlange.zetic.ai/p/Steve/RetinaFace)
TL;DR: Successfully deployed RetinaFace with ZETIC.MLange achieving 1.43ms inference on mobile NPU!
๐ Complete Performance Analysis:
Latency Comparison:
- NPU: 1.43ms (Winner! ๐)
- GPU: 3.75ms
- CPU: 21.42ms
Accuracy Metrics - SNR:
- FP16: 56.98 dB
- Integer Quantized: 48.03 dB
(Precision-Performance: Excellent trade-off maintained)
Memory Footprint:
- Model Size: 2.00 MB (highly compressed)
- Runtime Memory: 14.58 MB peak
- Deployment Ready: โ Production optimized
๐ Technical Implementation:
(Runnable with Copy & Paste at the MLange link!)
๐ Device Compatibility Matrix:
Tested on 50+ devices including Samsung Galaxy series, Google Pixel lineup, and Xiaomi devices, iPhones and iPads.
Consistent sub-5ms performance across the board!
๐ Applications Unlocked:
- Real-time AR/VR face tracking
- Privacy-preserving edge authentication
- Live video processing pipelines
- Mobile security applications
- Interactive camera filters
The democratization of high-performance computer vision on mobile devices is happening NOW! This study proves that complex CV models can run efficiently on consumer hardware without compromising accuracy.
Want to reproduce these results? Check out the benchmark methodology and implementation guide!
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Make your ZeroGPU Spaces go brrr with PyTorch ahead-of-time compilation
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**Wikipedia Monthly's September edition is now live ๐**
Highlights of this edition:
ยท ๐ฃ๏ธ 341 languages
ยท ๐ 63.1M articles
ยท ๐ฆ 86.5GB of data
This update also solves upload issues in the August edition where some languages had missing parts. Happy data engineering!
omarkamali/wikipedia-monthly
Highlights of this edition:
ยท ๐ฃ๏ธ 341 languages
ยท ๐ 63.1M articles
ยท ๐ฆ 86.5GB of data
This update also solves upload issues in the August edition where some languages had missing parts. Happy data engineering!
omarkamali/wikipedia-monthly
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