How YOLO Object Detection Works
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- Опубликовано: 19 июн 2024
- Here we introduce YOLO (You Only Look Once), a powerful object detection framework capable of real-time detection using a simple yet effective strategy.
Timestamps
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Introduction 00:00
DPM and R-CNN 01:35
YOLO algorithm scheme 02:50
Architecture 05:10
Target outputs 05:50
Non-max suppression 10:10
Loss function 10:45
Limitations 13:45
Summary 16:33
so, what yolo does is basically what I have been doing for these captchas for many many years? I love the video
This is the best explanation of YOLO I found. Thanks!
Glad you found it helpful!
Most thorough explanation I’ve come across
Simple , clear and excallent ! Thanks for the explanation.
This is the best explanation on YOLO! Thank you very much.
This was really clear and precise! thanks :)
This is the best explaination I found so far! From india 🇮🇳
very helpful and easy to understand Thanks
Very nicely presented
Cool!!!
The class probability map is only used to do loss calculations?
4:06 why we take square root of w, h?
Does this also work for YOLOv8? because YOLOv8 is different from other versions that use free anchor detection. Thank You
Hi! Your explanation was dope! Mind dropping the source or reference for that model accuracy-speed comparison table?
Appreciate your comment! The full table can be found in the original YOLO paper (arxiv.org/abs/1506.02640).
Why don't use cross-entropy for the class loss?
Good question...some later versions (YOLOv3 onwards) use binary cross entropy to enable multi-label classification. Not sure why mean squared error was chosen for YOLOv1.
not, I look as much as I want.
Do I understand correctly that in NOOBJ part ground truth Ci is always zero and in OBJ part ground truth Ci is in [0,1]? ruclips.net/video/svn9-xV7wjk/видео.htmlsi=pA0UvT1dzNKjNEqv&t=766
On Ground truth slide seem to be a mistake. 8:06 Check x = (16-10)/10 = 0.6 should be and similarly y = (44-4*10)/10 = 0.4
It's a modulus operator
4:10 are u robot?
Click sll the boxes that contains bicyle