Intel Neural Compute Stick 2 / Movidius AI Test

** ** * ***** ***** ** video ************ **** ************* ****** *********** attention *******'* ******** ****** *****. ******,******** *** ********* **** ***** **** generation ** ** *********** *** *** ****** *.

*** *** **** ** ***** ****** X ***** *******?

**** *** **** *** ***** ******* of *** ****** * **** (*** the ****) *** ****** ******* ****** this ******.

Intel / ******** / ******* ******* ***** ********

******* **** ******** **** *** ***** / ******** ***** ****** **** ** deep ******** / ********** ************. ** the **** ** ** ***** ** a ****** * ****. ***** **** be **** **** ** **-***** ****, the **** **** *** **** ******** models *** ** **** ****** *************** of ******, *******, *****, ***.

**** * **** ***,***** ************ ****** ********** ** '****** ******* ******' (***). ***** *** ***** *** had *** ******** / ***** ******** Myriad * ****, **** *** *** has *** *********** *******, ***** ***** ****** "******** *** the *********** ******** ** ******** ***********".

**** ****** *** **** *** ****** it.

Areas ********

****** **** ******, ** ***** ******* across:

• **** ********* ***** *****'* ****-*********-******-**** *****
• ******* ****** ***** **, ****** * and ****** *
• ***, *** *** *** *********** ********* across *******
• ******** ********** ** *********
• **** ******* *** ******

Key ********

*** *** ******** **** *** ******* are:

• Myriad * ~= *.** ****** * ***: The NCS2 ran roughly 2.7x frames-per-second more than the NCS1 in their face detection model we tested. Intel says that different optimizations and measurement changes would increase this significantly (discussed in caveats below).
• Faster **** *** (**): The NCS2 / Myriad X outperformed the i7-7700K (a top-of-the-line in 2017) chip tested here, a significant advance given the Myriad X being significantly lower cost and lower power consumption.
• Little/no ******** ****: Our tests noted no differences in output video accuracy in line's feedback that OpenVINO's model-optimization step should be considered "lossless." They pointed out that allowing lossy model compression (when converting a model to be run on Myriad; eg FP32->FP16 data-type downsizing) would cause issues with vendors training a model with certain expectations, and having a different (lossy) model for inferring.
• Model ************* ** ******: While Intel has mentioned YOLO support for OpenVino, the current YOLOv3 is not yet compatible on Myriad chips due to a particular network layer. ***** **************** ***** *** ******* ********** ****** to ** *** ** ***, *** YOLOv3 ** *** *** ********** ** Myriad ******* *** ** * ********** network *****. **** ** & **: Intel ******* ***** *** ** *** on ******, *** *** ***** *******.**********+***: The ***** ** **** ****** **** variety, ************ ***** ****-*********-******-**** (********** ***** with * ******-**** ********).*********+***: *** ***** pending. ** **** ******* ****** ****** from ****** ** *****'* *** ******.

*** ****** ***** ******** *********** *******:

Caveat ** *** / *****

***********, *****'* *** ******* *** **** higher **** *** ***, *** **** reasons. **, *****'* ****-*********-******-**** ***** ******* 250 *** ** ***, **.* ** Myriad2 (**** ***** ******** ********** ******* numbers ** **** ******).

* *** ****** ** *** ***** Intel ******* ***: (*) ** *** Python, **** ******* **** ***** *++ code; (*) *** ***-******* ******** ******* code (** ****** *******/******* ******), *** isolating ***** ****; (*) ** *** not ***** ******* ************ ******** ** video ***** *******, ***** ** ***** into ********.

Video **********

***** ** * ***** ********** **** reviews *** *******:

Questions / ******** / **** ** **** ****?

** **** ******* ******* ** *** NCS2 / ****** * ***** **** other ********* *** **** **** ****** over *** **** *** *****. **** would *** **** ** *** ******?