YOLOv5 Released Amidst Controversy

By: Zach Segal, Published on Jul 27, 2020

YOLO has gained significant attention within video surveillance for its promise of better, faster video analytics. However, 2020 has proven to be controversial for the platform.

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Inside this report, we:

  • Explain what happened to the Original Team Including XNOR.ai
  • Describe the Future of YOLO Development
  • YOLOv5 Controversy
  • YOLOv5 vs YOLOv4 vs YOLOv3
  • Real-World Impact

**** *** ****** *********** attention ****** ***** ************ for *** ******* ** better, ****** ***** *********. However, **** *** ****** to ** ************* *** the ********.

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****** **** ******, **:

  • ******* **** ******** ** the ******** **** ********* XNOR.ai
  • ******** *** ****** ** YOLO ***********
  • ****** ***********
  • ****** ** ****** ** YOLOv3
  • ****-***** ******

[***************]

Executive *******:

** ****, ****** *******, along **** *** ******* and * *** ******, released ****** (*** **** Look **** ******* *), a ***** ****** ********* algorithm **** *** **** and ********. ****, ***** releasing ******** * *** 3,********** *** ******** **** privacy ***************.**, ***** *** ******* by *** ***** ******* of *** ******** **** team, *********** ** ************* ********* Apple, ******* *** ***** of **** ** ****.

****, ****** *********** **************, ***** *** ************ improvements **** **, *** was ****** ********* ** the **** **** ** the **** ******* ************ *** ******** ** large.****, **** ***** ******/*********** released******, ** *** ********* controversial ******* *** ************ were ** **** ** use (*** ******** ** speed) **** **** (********* Bochovskiy) ******* ** ** nothing **** **** * tweaked ******* ** ******. Also, ****** *** ******** versions, ***** *** ** paper ******** ** ** (so ***** *** ** peer-review *******) *** ** was ********* **** ***** to * ******* *******. Regardless, ****** ***/** ****** have *** ********* ** make ****-**** ****** ********* more ************ ********.

What ** ****

**** ** *** **** Look **** ****** *** a ***** ** ****** detection ********** **** *** to ** ****** ********* accurately *** ***********. **** introduced *** **** ** using *** ****** ******* for ******** *** *** class **********, ******* * two-step ******* **** *** piece, ******** ****** ************. They **** ***** ********** in**** ** * ******** student,****** ******, *** **** garnered **** ** *********. He ******** *** **** updated ********, ****** *** YOLOv3; ****** ****** *** research *** ******* *** industry.

Why ******* **** ****

******* *****'******** ************ *** ******* concerns' ** ******* ** stopped:

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XNOR.ai ** *****

*** **** ** *** original **** **** ******* XNOR.ai,***** *** ****** ** Apple *** $*** *********** ****. *** ** Apple’s ******* ***** ** privacy *** ***-**-*** ********, its ****** *&* **** probably *** ** *********. Indeed, ****.** ********** **** and ******* ******** *** analytics ** ***** *********.

The ***** ** **** *****

**** **** **** *** seen *** *** ***** create***************. *******, ***** ****** Bochkovskiy’s ****** *********************** ********* ******** ******, ***** *** ******************* ***** ******/**********’* ******.

The ***********

****** ** *** ****** an *********** **** ******. YOLOv5 ** ****** *** less ******** **** ******, but **** **** ********. Specifically, ****** ******** **** versions **** ******** *******, YOLOv5 **** ******** ** PyTorch, * ****** **** deep ******** *********.

**** ********* ***** ******* why ** ** ***** used ************ **************** (******* **** ******* contests).

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****, ****** ******** ******, ****** was ******** *********-********* ** a ******* *******, ******* a ****-******** ***** ******** to **. **** ***** the ****** **** ****** made **** *** ******, there ** ********* **** when *** **** ******* will ** **** *** what **** **** **** like, *** ** *** be **** ** * PR **** ******* ** an ******* ** ******* computer ******.

Improvements ******* ** *** **

****** *** *********** ************ over ******** ***** ** ***** and ********. ** *** MS **** ****** ********* dataset, ** ** *** YOLOv4 ******** ~**% ****** AP50 (~** **. ~**) than ******.

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YOLOv4 *****

*********** ***** *** ******, ********* *** **** accurate *** ******* ******* are ******.**% **** **** ~** fps ** ** ****** 1080Ti (~$***).

** ********, ******-**** *** significantly ******* **** ** 40.2% ***** **** **** 8x ******** ** *** at ~*** *** **** the **** **** **(~$***).

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Potential ***** ************ ************

**** *** **** *** years, * ****** ** video ************ ********* **** used ** ******* ********** of **** ** *** in ********** ***** *********. While **** ******** ***** on ** ********** (******* **** ********), *** *********** *** video ************ ************ *** be ******* ********* *********** categories **** ***** ************ users *** ** **** interested (*.*., *** * categories ** *******).

** **** ***, ********** of ****** ***** ******* further ************ *** ***** surveillance *** *** ******* over *** **** **** within **** *** ****** the ********* *** ****** gains **** **** ********.

Comments (4)

**** ****!

***** ***

** (*********) **** ***** YOLO4 *** *** ****** detection ** ********* ********** and ******** ** **** Mobilenet ** *** ********. All ***** ******** **** trained ** *** *** and ****** ** *** same **** **** **** CCTV ******. ********* (********* false ********* ****) *** nearly *** **** *** all ********, ****** (********* detection ****) ** ****** the **** *** ***** and ********, *** **** are **** ****** **** Mobilenet. ******** ** ****** 3 ***** "******" **** Mobilenet, ***** ** ** times ****** **** *********. As * ****** ** decided **** *** *** goals ******** ** ****** than *****. ** ***** is *** *** **** choice *** ****.

***** *** *** ******** this **. **'* ***** to **** **** ********** have **** **** *** in ******** *** ***** and *** **** **** picked.

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