YOLOv5 Released Amidst Controversy
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.
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
Executive *******:
** ****, ****** *******, ***** **** his ******* *** * *** ******, released ****** (*** **** **** **** version *), * ***** ****** ********* algorithm **** *** **** *** ********. Then, ***** ********* ******** * *** 3,********** *** ******** **** ******* ***************.**, ***** *** ******* ** *** other ******* ** *** ******** **** team, *********** ** ************* ********* *****, ******* *** ***** ** **** in ****.
****, ****** *********** **************, ***** *** ************ ************ **** v3, *** *** ****** ********* ** the **** **** ** *** **** lineage ************ *** ******** ** *****.****, **** ***** ******/*********** **************, ** *** ********* ************* ******* the ************ **** ** **** ** use (*** ******** ** *****) **** many (********* **********) ******* ** ** nothing **** **** * ******* ******* of ******. ****, ****** *** ******** versions, ***** *** ** ***** ******** to ** (** ***** *** ** peer-review *******) *** ** *** ********* with ***** ** * ******* *******. Regardless, ****** ***/** ****** **** *** potential ** **** ****-**** ****** ********* more ************ ********.
What ** ****
**** ** *** **** **** **** models *** * ***** ** ****** detection ********** **** *** ** ** object ********* ********** *** ***********. **** introduced *** **** ** ***** *** neural ******* *** ******** *** *** class **********, ******* * ***-**** ******* into *** *****, ******** ****** ************. They **** ***** ********** ****** ** * ******** *******,****** ******, *** **** ******** **** of *********. ** ******** *** **** updated ********, ****** *** ******; ****** ending *** ******** *** ******* *** industry.
Why ******* **** ****
******* *****'******** ************ *** ******* ********' ** reasons ** *******:
XNOR.ai ** *****
*** **** ** *** ******** **** team ******* ****.**,***** *** ****** ** ***** *** $200 *********** ****. *** ** *****’* ******* focus ** ******* *** ***-**-*** ********, its ****** *&* **** ******** *** be *********. ******, ****.** ********** **** and ******* ******** *** ********* ** other *********.
The ***** ** **** *****
**** **** **** *** **** *** new ***** *********************. *******, ***** ****** ***********’* ****** has******************** ********* ******** ******, ***** *** ******************* ***** ******/**********’* ******.
The ***********
****** ** *** ****** ** *********** over ******. ****** ** ****** *** less ******** **** ******, *** **** user ********. ************, ****** ******** **** versions **** ******** *******, ****** **** natively ** *******, * ****** **** deep ******** *********.
**** ********* ***** ******* *** ** is ***** **** ************ **************** (******* **** ******* ********).
****, ****** ******** ******, ****** *** ******** partially-completed ** * ******* *******, ******* a ****-******** ***** ******** ** **. This ***** *** ****** **** ****** made **** *** ******, ***** ** confusion **** **** *** **** ******* will ** **** *** **** **** will **** ****, *** ** *** be **** ** * ** **** instead ** ** ******* ** ******* computer ******.
Improvements ******* ** *** **
****** *** *********** ************ **** ******** ***** ** ***** *** ********. On *** ** **** ****** ********* dataset, ** ** *** ****** ******** ~30% ****** **** (~** **. ~**) than ******.
YOLOv4 *****
*********** ***** *** ******, ********* *** **** ******** *** fastest ******* *** ******.**% **** **** ~** *** ** an ****** ****** (~$***).
** ********, ******-**** *** ************* ******* AP50 ** **.*% ***** **** **** 8x ******** ** *** ** ~*** fps **** *** **** **** **(~$***).
Potential ***** ************ ************
**** *** **** *** *****, * number ** ***** ************ ********* **** used ** ******* ********** ** **** to *** ** ********** ***** *********. While **** ******** ***** ** ** categories (******* **** ********), *** *********** *** ***** ************ applications *** ** ******* ********* *********** categories **** ***** ************ ***** *** be **** ********** (*.*., *** * categories ** *******).
** **** ***, ********** ** ****** could ******* ******* ************ *** ***** surveillance *** *** ******* **** *** past **** ****** **** *** ****** the ********* *** ****** ***** **** this ********.
** (*********) **** ***** ***** *** the ****** ********* ** ********* ********** and ******** ** **** ********* ** and ********. *** ***** ******** **** trained ** *** *** *** ****** on *** **** **** **** **** CCTV ******. ********* (********* ***** ********* rate) *** ****** *** **** *** all ********, ****** (********* ********* ****) is ****** *** **** *** ***** and ********, *** **** *** **** better **** *********. ******** ** ****** 3 ***** "******" **** *********, ***** is ** ***** ****** **** *********. As * ****** ** ******* **** for *** ***** ******** ** ****** than *****. ** ***** ** *** the **** ****** *** ****.
***** *** *** ******** **** **. It's ***** ** **** **** ********** have **** **** *** ** ******** use ***** *** *** **** **** picked.
* **** **** ***** *** ************* * ******* **-**** ***** *********** ******. ** will ** *********** ** *** ***/** the ********** **** ***** ******** *** combined ** **** ****** *** **** accurate ****** ********* ******.
**** ****!