TLDR yolov4 has an outstanding accuracy and an even more impressive
detection speed compared to all currently available detectors and
the best thing about this paper is that you can do all this from your home I
recently made a video explaining the basics of the initial you only look once also known as
the yellow algorithm and now four years later a fault version of this algorithm has just been
released and it has the best real-time object detection accuracy to date it is essentially a
real-time object detector just like the initial yellow algorithm but much better I invite you to
check my first euro video before continuing this one if you are not familiar with it
there's a card on top right of the screen and a link in the description this fourth version
has been recently introduced in April 2025 Alexei Bukowski and all in the paper euro V for optimal
speed and accuracy of object detection the main goal of this algorithm was to make a super-fast
object detector with high quality in terms of accuracy typically object detector architectures
are composed of several components first there is the input which is your image next we have
the backbone and the neck which take the image as input and extract the feature maps using a
deep neural network finally we have to make and handle these predictions using an object
detector like yellow or SSD the Optoma is this architecture and use the latest yellow version
Yolo v3 as the end of China object detector there are a lot of factors to consider when
you try to optimize this architecture and this is the biggest success of this paper I
won't elaborate about all these optimizations because there are way too much to go in depth
in a single video but I invite you to read the paper if you are interested about this part of
their work but most importantly they introduced a new method of data augmentation called mosaic
and self adversarial training then they tested with a lot of different features
multiple classifiers multiple detectors multiple backbones and a multitude of
hyper parameters they had so much parameters to test that they use a technique called ablation
study which is used to evaluate which parts of the input are relevant to the network's output
by systematically removing these input parts as you can see euro v4 has a significant upgrade in
terms of speed and performance compared to its previous versions and other object detectors
they tested it at multiple GPU architectures like Maxwell Pascal and Volta and here are the results
in summary yellow beaver has an outstanding accuracy and an even more impressive detection
speed compared to all currently available alternative detectors and the best thing about
this paper is that you can do all this from your home it's the first real-time object detector that
you can train and operate on a conventional GPU instead of having to use a large number of GPUs
which no one has access from their home they made this happen with the optimization for parallel
computations rather than the low computation volume theoretical indicator normally used
called B flop it is a massive improvement for multiple fields like autonomous vehicles poker
cheat detection and more where object detection is needed in real time of course this was just
a simple overview of the new Yolo v4 algorithm I strongly recommend to read the paper and the
super clear major imposed by the authors linked in the description for more information if you want
me to cover a specific term in a next video leave it in the comments below please leave a like if
you learn something and subscribe to the channel to not miss any further terms clearly explained