Neurala Brain Builder Tested (Train Your Own Analytics)

By Rob Kilpatrick, Published Nov 17, 2020, 08:53am EST

Do you want to train your analytics? For example, to detect a certain type of fish, tool, bird, buggy, etc.? While most analytics are pre-trained for people, vehicles, bikes, etc., Neurala says their Brain Builder app lets you train analytics for the specific objects you most care about.

But how well does this training work in real scenes?

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We tested the Brain Builder app on the new Security and Safety Things platform (tested here), examining:

  • Does it properly identify the objects it is taught?
  • Can it identify the object in a cluttered scene?
  • Can two objects be identified at the same time?
  • Can objects be identified at different angles?
  • How hard/easy is it to train?
  • How does it compare to Bosch AI Camera Trainer?

****: **** ** * test ** *******'* ***** functionality ***** ***** ********&** ***. ******* **** ****** an **-******* *** ***** version **** **** ******** and *********** ** ***** systems.

*******

***** ** *** *****, Neurala ***** ******* ********* identified ******** ******* **** it *** ****** ** close ****** *** **** uncluttered ***********. ******** *** simple, *********** **** ******** a "*****" ****** **** the ****** ******* ** the ***'* ********* ****.

*******, ** *** ******* limitations, **** ********* ******* in ******** ********* ******, when ******** ******* *** viewed **************, ** **** with ** (~*.** ***), or **** ******* *** carried ** ********. ************, objects **** ** ******* at ********* ****** *** proper *********** **** ********* points ** ****, ****** to ******* ***** *** error ** ** ** unclear *** **** ****** are ******** *** ******** recognition ** **** *****.

******* ** ****, ******* is ****** ********** *** general ******** ************, *** a ****** *** ** process **********, ******* **********, etc.

Neurala ********

******* ******* *** ********* feedback ** *** *******:

** ********* * ************* app *** *&** ** support * ***-**** ************* of * ****** *****-*** where *** ****** *** placed **** *** ****-******** produce ***** ******* ** order ** ****** *** need *** * **** to ******** ****** *** the ******* ****. ** with **** ** ****, certainly **-******** *****, *******, the ******** ** **** hand, ***. **** ****** performance.

************, ********* *********** ** cluttered ******, ******* ****:

**** *** ** ***** a ***** ***** **********, so ** **** **’* looking ** **** ***** of ********** ****** *** region ** ******** ** a *****. *** *** cluttered ***-**** ** ** successful, **’* ****** ********* a ********* ***** **** looks ** ****** ******** and/or ********* ***** ****** the ***** (**** ******** represented **** * ******** box ****** ****.)

Vs. ***** ****** *******

***** ******* ****** ******* similarities *********'* ** ****** *******, ** **** *** be ******* ** ********* specific ******* ** *** field. *******, ***** ****** Trainer ****** ** ******* false ********* ** ****** in ******* ******, ***** Neurala **** ***. ************, Camera ******* ****** ** longer ********* *** ** cluttered ****** ** ****, scenarios ***** ****** ****** detection ** ***** *******.

Neurala ******** *******

** ***** *******, ***** place *** ******* ****** in ***** *******'* ********* zone *** ***** *** "Train" ****** *** **** it. ** ***** ************* is *********, ****** ********** training *** ** ******** to ********* ********* ****** (discussed *****).

Multiple ****** *** ******* ********** ********

** ***** ** ********* objects ** ******* ******, Neurala **** ** ******* multiple *****. *** *******, below, *** ***** ** taught **** ** *** angle *** ****** **** pointed ****** *** ******.

***** ******** ** ********** angles, *** ***** ** recognized:

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Objects ********** ********

**** ******* **** ****** to ***** ******* **** were ********** ******** ***** when ********** ** *** scene.

Short *********** ****

******* ***** ********** ******* in *** ***** **** they **** ******* *****, present ** *** ***** for ~* ******.

Objects *** ********** ** ********* ******

***** ******* **** ********** recognized ** ****** **** relatively ****** ********** *********** or ***** *******, **** the ****** *** ***** at ********* ******, ** objects **** **********. *** example, *****, ******* ***** to ****** *** *** water ****** (***, ******) on * **** ****** with ***** *******.

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Object *** ********** ** ***** ****** ** ****

************, ** *****, **** realistic ****** ** ****, Brain ******* *** *** identify *******. *** *******, it *** ****** ** locate * ***** ** a **** **** ** other ***** ***** ** is ****** *** ****** on *** ****.

***** ******* **** ****** to ******** ******* ***** held ** * ****** walking ******* *** *****. Below, *** ******* ******* the **** ***** ***** was ********** ****** ** the *** ** ***** range, ~**', *** ** is *** **********.

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Objects ******** ** *** ***** (~***) ** ***** *****

****** **** **** ********** when **** ** ***** of *** ****** ** low ***** (~* ***), but **** ** ***** range.

No ********* ** **** **** ** (~*.****)

*******, *** **** ****** was ****** ** * dark ***** (~*.****) **** IR.

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No ************ ***********

** *** *****, **** one ****** ** * time ***** ** ********** in *** *****, ******* two ******* ***** ****** in ***** ******* **** picking *** ****** ** classify.

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S&ST *** ** ************ / **** ** ****

*** ***** ******* *&** app **** *** ********* to ***** *******. ** contains **** *** ****** monitoring **, **** ** settings ** ****** ****** displaying *** ******** ****** in *** *****-********* ******.

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S&ST *** *****

*** ** ** *** Neurala ***** ******* *** is ******, ********* ********* to ***** *&** **** by ********** ** ** the *** *****, **** pushing ** ** ******* cameras. **** *********, ***** may ****** *** **** UI ** ***** ******* or ****** ******.

S&ST *** **. **** *******

******* ******** ***** ******* as * **** ** the *&** *** *****, with * ******* ********** and ** **** *****.

*******, **** **** ***** a ****-******** ******* ** this ***********. ******* ******** that *** **** ******* is ********* ********** **** other *******:

********* *** ****** ******** by ***** ******* *** integrated **** ***** ************ that ******** *** ***** outputs ** ******* *** business ***** ** *** end ******** (*.*. ** trigger ** ***** ** a ******** *****, ** eject ********* ******** **** the ********** **** ** to ******* * ***** stick ** * ****-***** out *******.)

**** **** ***** * separate ********* *********** ***** can ** **** ** run ****** ******* ** Brain *******, *** **** not ******* ******** ********.

******* *** *** **** version ** ***** ******* starts ** $*,*** *** / **** ****. ********* starts ** $*** / year. **** ** ***** platforms *** ** *** on-premise, ** *** *****, or * ****** ** both.

Versions ****

*** ********* ******** **** used ****** *******.

  • ******* ***** *******: *.*.*

Comments (6)

************* ******** ***** ** ***'** into **** **** ** thing.

***... ******? **.**? ** Teachable ******* ******** ******** ****?

**** ***** **** **** the **** ****. (**** the ******** ********* ** erector ****--***** **** ****** cool.)

* ** ******* ** your ******* **** *** drill ******* **** ***** happen ** *** **** the **** ****** ********. Would *** ***** ***** be ******** ** ** Neuarala ********* *********** ******** to *** *** *** detector *** ******* **? In ********, **** *** train * ******** ***** can *** ******** ** moved ** ***** ******* equipped ******* ** **** each ****** **** ** be ******* *************?

*********** ** *** ******** to *** ***** ** view **** ** *** trained **, *** ***** take ** ******* *** it ***** ***** ****** the ****** ** **** as *** **** ** within ~*' ** *** camera. **** ****** ** trained ************* ** **** other *** ****** ** moved ** ******* ** if *** **** ** object ******* ** ******* camera *** **** ** do *** ******** *****.

*** **** ******** ***** analytics ** ********* ********* Cognitive ********?

********, ****** *** **** first *******. *** ******, here ** *** ******* page:********* ********—**** *** ** Developers | ********* *****

** **** ***. ** general, ** ***** ** testing ** ********* **** are **** *** *** users *** *********** ** deploy ******* *** *********** effort, ***** **** ** the **** ****** *** case.

**** ****, *****'* ********* or ***** **** **** Google ** ****** ***** be ****** *** ***** actually ********** ***** *** services.

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