readme: ---
  license: cc-by-nc-4.0
  ---
  
  # **Our Mission for embedded AI**
  
  In our mission to unleash AI on the edge, Datakalab has applied its proprietary quantization method on classical architecture and converted everythong to run in OpenVino® format for seamless compatibility with Intel® hardware such as Intel® core of 12th generation or Xeon® server
  
  Download any of the quantized models below to put your deep learning models on Intel® hardware.  Enjoy and let us know what you think!
  
  * Intel® Deep Learning Boost (Intel® DL Boost)
  * Intel® AVX-512 
  * Intel® AVX-2
  
  
  If you are looking for  further compression methods (continual learning, pruning, batch norm folding, etc.) to fit your models on Intel devices, we’d love to hear from you!
  
  
  # **Latency on I5 12500TE®  with less than 1% of accuracy drop**
  
  | **Model name**    | <p align="center">  **FPS in int8** | <p align="center">  **FPS in fp32** | <p align="center">  **Performance Ratio[int8/fp32]**| <p align="center">  **Relative Accuracy Drop[(fp32-int8)/fp32]**| <p align="center">  **Memory Size Ratio [fp32/int8]** |
  | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |
  | Mobilenet V2  | <p align="center"> 1057 | <p align="center"> 424 | <p align="center"> x 2.5 | <p align="center"> 0,12% | <p align="center"> x 3.6 |
  | Resnet50  | <p align="center"> 263 | <p align="center"> 76  | <p align="center"> x 3.5 | <p align="center"> 0,13% | <p align="center"> x 3.9 | 
  | Efficientnetlite b0 | <p align="center"> 1165 | <p align="center"> 659 | <p align="center"> x 1.8 | <p align="center"> 0,1% | <p align="center"> x 3.7 |
  | YoloV3  | <p align="center"> 34.25  | <p align="center"> 9.75 | <p align="center"> x 3.5 | <p align="center"> 0.93% <sup>1</sup>  | <p align="center"> x 3.75 |
  
  <sup>1</sup>  Accuracy Ratio on mAP results
  
  # **Thanks**
  All those benchmarks where made using Intel® DevCloud Thanks a lot **@Intel®**
  
  # **Contact us**
  
  Feel free to reach out by email at **hello at datakalab.com**