Apr 23rd, 2020 — YOLOv4 was released…
…June 10th 2020, YOLOv5 was also released.
Marvelous ain’t it…at how fast we are progressing in our research and technology. I mean to get the next generation of the popular object detection framework so soon after its predecessor was just released. Is YOLOv5 really here or is it a ruse ? We’ll investigate the evidence as objectively as possible, right now in this article, so stay tuned.
For those who don’t know what YOLO is, it a real-time object detection framework and stands for You Only Look Once. Meaning the image is only passed once through the FCNN or fully convolutional neural network. I will not go into the technical details of how YOLO works, as I’ve already have 2 videos on my YouTube Channel explained YOLOv1 originated by Joseph Redmon et. Al. all the way to YOLOv4 upgrade by Bochkovskiy et. al.
_For those of you are interested in my course, there will be a link in the description where you can enroll in the full YOLOv4 course when it gets released. We cover the implementation of YOLOv4, training and inference as well as building cross platform object detection apps using PyQT. _Click HERE
Part 1 — What has Occurred.
Okay, so back to YOLOv5. Glenn Jocher the founder and CEO of Ultralytics released its open source implementation of YOLOv5 repo on GitHub [https://github.com/ultralytics/yolov5], which supposedly said to be the state of the art among all known YOLO implementations according to the Ultralytics GitHub page.
Based on their results is shows how well it outperformed EfficientDet which is Googles open source object detection framework, but what I find strange is that while they do not explicitly show their comparison with YOLOv4, YOLOv5 is said to be able to achieve fast detection at 140FPS running on a Tesla P100 in comparison to YOLOv4 which bench-marked at a measly 50 FPS stated on an article published on the Roboflow blog titled YOLOv5 is Here: State-of-the-Art Object Detection at 140 FPS by Joseph Nelson and Jacob Solawetz [https://blog.roboflow.ai/yolov5-is-here/] .
Furthermore, they mentioned that “YOLOv5 is small at only 27 Megabytes”. What, that is ridiculously small compared to the 244 megabytes of YOLOv4 with darknet architecture…Whaaat… That’s nearly 90 percent small than YOLOv4. That’s craaazzy.
In terms of accuracy, “YOLOv5 performs on par with YOLOv4.”
So essentially looking at the claims in which YOLOv5 is said to be Extremely fast,** light** in terms of its model size but** on par** in terms of accuracy with the YOLOv4 benchmark.
Just food for thought if PlayStation or Xbox released a new console that had the same graphics performance, maybe faster load times but in a smaller package would that constitute this new console as a next gen console or just a light-weight version of the current-gen console like the PS4 Slim or Xbox One S? Let me know in the comments what you think.
Part 2 — Questions
So some further questions that crossed my mind are can you claim or name a technology even opensource ones as your own even though you were not the original creator. Eeeh…Im not sure, this one is a debatable one. Does using the exact same framework and just modifying a bit give you the right to brand it as your own but with an increment in the version number, in this case YOLO with version 5. Well I guess this depends on the original creator or creators of the framework. You may or may not have heard of the original creator Joseph Redmon whom tweeted in February 2020 that he would step down from his research of his brain child YOLO due to the societal impact their work was having. He stated:
“ I loved the work but the military applications and privacy concerns eventually became impossible to ignore”.
Redmon had created 3 iterations of YOLO in partnership with Ali Faradi.
Now later this year YOLOv4 appeared in April 2020 but by none of the original authors but rather by Bochkovskiy et. al. The paper was published and peer reviewed, GitHub code uploaded to the AlexeyAB/darknet repo and everything seemed fine, the technological upgrade was great and well received in the computer vision community. So does this mean that if Bochkovskiy et. al. did it, then anyone else can take the YOLO framework, make some improvements and increment the version number? Well that’s exactly what happened.
Glenn Jocher, you know the founder and CEO of Ultralytics dropped YOLOv5 like a bomb, BOOM. So you must still be wondering… okay Ritz… tell us now… so is YOLOv5 legit or it a ruse or a lie. Okay, okay, okay I know you want the answer, but hold on a bit right, lets first examine the evidence.
#artificial-intelligence #computer-vision #deep-learning #yolov4 #yolov5 #deep learning
