Article
Introducing Storage Buckets on the Hugging Face Hub
- +10
ā¢
191
SitrakaForler
SitrakaForler
Ā·
AI & ML interests
NLP; Navier-Stokes; Fluid Mechanics
Recent Activity
upvoted an article about 1 month ago
Introducing Storage Buckets on the Hugging Face Hub liked a model 5 months ago
ProsusAI/finbert liked a Space 5 months ago
HuggingFaceTB/smol-training-playbookOrganizations
None yet
upvoted an article about 1 month ago
liked a
model 5 months ago
liked a
Space 5 months ago
reactedto yeonseok-zeticai's post with š 7 months ago
Post
3731
šÆ 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!
š Complete Performance Analysis:
Latency Comparison:
- NPU: 1.43ms (Winner! š)
- 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: ā Production optimized
š Technical Implementation:
(Runnable with Copy & Paste at the MLange link!)
š 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!
š 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!
š Complete Performance Analysis:
Latency Comparison:
- NPU: 1.43ms (Winner! š)
- 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: ā Production optimized
š Technical Implementation:
(Runnable with Copy & Paste at the MLange link!)
š 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!
š 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!
upvoted an article about 1 year ago
Article
Introducing smolagents: simple agents that write actions in code.
- +1
ā¢
1.19k
updated a
Space about 1 year ago
liked a
Space about 1 year ago
published a
Space about 1 year ago
liked 2
Spaces about 1 year ago
[bot] Conversion to Parquet
1
#1 opened over 1 year ago
by 
parquet-converter
liked a
model almost 2 years ago
liked a
Space over 2 years ago
liked 2
models over 2 years ago
liked a
Space over 2 years ago
updated a
Space almost 3 years ago
liked a
Space almost 3 years ago
liked a
Space about 3 years ago
liked a
model about 3 years ago
