What is YOLO algorithm? | Deep Learning Tutorial 31 (Tensorflow, Keras & Python)
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- Опубликовано: 18 июн 2024
- YOLO (You only look once) is a state of the art object detection algorithm that has become main method of detecting objects in the field of computer vision. Previously people used techniques such as sliding window object detection, R CNN, Fast R CNN and Faster R CNN. But after its invention in 2015, YOLO has become an industry standard for object detection due to its speed and accuracy. In this video we will understand the theory behind how exactly YOLO algorithm works. In next video we will write code to detect objects using YOLO framework.
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The best explanation for YOLO! It's really helpful. Thank you.
Among all the yolov explaining videos this one makes the most sense! Thanks
Awesome work Sir, You explain such complicated things in a way, it feels like cakewalk to understand. Thanks alot . Please make full python yolo implementation for video inputs.
What an awesome video! You really know how a student thinks. You answered all my questions - even the ones that I didn't realize I had! This was some excellent video format and pacing. I have liked and subscribed.
thanks mate, went through a couple of videos and your's the one that explain it the best
My Deep Learning teacher couldn't explain this in 3 weeks the same way you did in 16 minutes, thank you very much.
so true
I think you didn't concentrate to your teacher lecture like you did in this video
This is the best explanation that I have not seen any where
Only once I watched and got knowledge on yolo
Thank you so much for this knowledge sharing
Great explanation of YOLO. And I need to say thank you for all your tutorials. I learnt a lot from you. Keep it up!
I really like your style of explanation. It's very clear and informative.
Glad it was helpful!
Such a perfect introduction to YOLO. Thanks!
I like this video very much. You explained the working of YOLO very simple , crystal and clear way. Thank you very much. Expect more.
This is a brilliant tutorial for YOLO. Thank you so much!
Excellent introduction to YOLO. Looking forward for code deployment video
I used YOLO before I understood what it was, thank you for helping me understand how YOLO works
Amazing as always! Thank you for providing this information and helping unravel important topics
it my first time around and i have already got a good level on YOLO...thanks for explanation///
Such a great communication happening in this video. The awareness of your audience at 8:15 is amazing. While it's true that "communication is what the listener does", to be a communicator, you must have empathy. Be proud of yourself for this.
thank you sir .. you have explained the content in very good manner. . with coding from scratch and i like it ... have a very nice moring..and many many best wishes from me to you !
Thank you very much sir !!! Egarly waiting for next part
My God which kind of perfect explanation is this wow I don’t what to say bro just God bless you
Yes.. there is no details about network!, its only about box encoding
Excellent explanation, you teach these topics in such a way that even a layman can understand
Gone thru many udemy courses, no one explains like you! Thanks for the efforts!
well worth watching. thanks for this. i had to pause where you said to as well. then I got it.
Glad it was helpful!
Sir your explanation is amazing in the field of data science
You clear the concept in 16 min thanks bro..
man, this was such a good explanation to YOLO!
Hi @codebasics, Very nice and clear explanation about YOLO. Is it possible for you to the presentation slides as well with us? So that we can refer to it when want revise the concept once again. Thanks once again.
hey, your video is so helpful...
It's badly in need of a video of HYPER-PARAMETERS TUNING in tensorflow
pls make a video about this topic
thank you so much
The best Explanation of Yolo thank you very much
Great explanation. The images helped to understand concept very easily, thanks
Thanks for the explanation. It's help me alot to understand yolo 👍
Hi man. Finally, someone that understands how to make a great video. I just see 15'' and got what I was looking for. I also want to watch the rest because it is well explained. thanks
Thanks, it's an excellent explanation, just what I needed.
Very nice, excellent description. Thank you!
The amount of good information and dogs in this video make me happy :)
Thank you very much. your explanation was great!
thank you so much for this, very easy to understand !
I really loved this video! Thank you!
Tks a lot sir, perfect explanation....
please make a full project on this from code to deploying
Thankyou Sir that was a very good and simple explanation of a complex algorithm :) Thankyousomuch sir
Hey man, good stuff. I am not a coder so pardon my question but do you know if YOLO7 or 8 can be used for body measurement and not just object detection?
Perfect and Clear Introduction to YOLO
Glad it was helpful!
I just love this video. It is the best explanation of the real 'concept' of YOLO algorithm. Thank you very much for your great effort and sharing the insight!
Congratulations on the video. Does yolo only recognize objects or does it classify emotions as well?
Thank you alot this explanation is all i ever needed
I am new to ML but still i understand what you have said bout YOLO great work
Best explanation online! Thanks for it. One question is that it is unclear how anchor boxes work?
Great Explanation. Thank you
Nicely explained everything Thank you sir
Great video. Did you do any image operation to detect overlap of two detected objects in same image ?
Hi, This is a very effective video. please provide a full project video with source code like face recognition project.
This video was fantastic. Thank you
Thank you so much for creating this video! You really explained everything clearly. I was looking for an explanation about YOLO on other platforms but no one could explain this as clearly as you have. May I ask if I can translate your video into Chinese and share it on a Chinese video platform for all the people who are interested in learning YOLO but failed to find an excellent video like this one? Really appreciate your effort in making this video.
best explanation... you are doing a great job.
Every software engineers should subscribe this best channel omg you are just fire 🔥 wow
You have explained things so well Ma Sha Allah, stay blessed and keep up the good work.
I watched a hour long video earlier and understood nothing, and now in just 16 min, I understood everything. Thanks a lot!
Glad you enjoyed it.
Thanks for sharing your knowledge
Nice work. You deserve more than one upvote. Sadly I can only give one.
Thanks for the brief explanation. Wanted to know how center of object can be decided here?
Thank you! Now it’s clear for me. Which app do you use for creating slides and graphic objects (tensors, tables, etc)?
Power point
I like it bro clear and simple explanations
Hello , i just have a quick query, which should i prefer Matlab or python for implementing a deep learning model used for classification of complex images(seabed characteristics) and could you please tell me the reason too...
Thank you for the practical tutorials.🙏🙏🙏
I have the following questions:
Can we use the saved weights from YOLOv7 instance segmentation for a classification problem?
We have a binary classification problem with 500 images, one class having only 30 images and the rest belonging to the other class. Can we extract features using instance segmentation on the images with fewer samples and then use all the features for classification?
Thanks for your wonderful explanation!
Glad it was helpful!
Sir
The explanation was very clear
And can I get the ppt that you used in the explanation
Thanks in advance
احسنت الشرح والتفصيل شكرا لك
Brilliant!!!!!!!!!
This is a great video, but the real magic of YOLO is in the loss function. Would you do a video on that?
Nice explanation sir. I have some queries. I want to predict one particular disease in earlier stage. May I combine deep learning(preprocessing), Yolo( for real time object detection), Unet( for segmentation) and CNN (for classification) in single project. Is it possible. Please help me sir. i expect your valuable suggestion.
Helpful. Nice work. Thank you so much.
Glad it was helpful!
Yeah! Very clear explanation.
Glad it was helpful!
Awesome!
Thanks for the video, it brought me back to light:)
I however still have a question: In the Yolo v1 paper it is described that the final convolutional output layer is a tensor of 7x7x1024 dimension (Darknet), then the detection follows, where grid cells dimension of 7x7 are defined. My assumption here is, since the dimension of the conv output the same as the grid cell's, can one say that one grid cell represents one pixel, hence the detection proceeds one 'pixel' at a time?
The size of the grid cells gets smaller and smaller as YOLO progresses. The last layer is the finest grid
Nice, I enjoyed the way that you explain it.
Glad you liked it!
Cool explanation, thanks!!
Glad I watched ur video ❤❤❤
you made our life easier
Best explanation till date
It's very nice explanation. Sir , can you please please make video on custom object detection.
excelente tutorial
Hello, does including negative photos to my dataset improve the detection of custom object?
Exceptional.
great video.. salute !
Totally Awesome
The best video!!
very nice explanation , btw either it will help to detect either brand logo is fake or not?
Splendid!
Excellent explanation
At 7:28, that looks more like 2 x the width of the grid cell. Why is it 3?
very clear, thx your video!
Glad it helped!
Really good explanation. I just have one doubt. How are bounding box measures calculated in yolo algo?
yes, it is the million dollar question :)
Excellent 👍
Great explainaition
Great video!
wonderful video very informative
Can you tell us how we use YOLO with a live stream to detect the different objects in the live stream as I have seen many videos that use YOLO with either image or video but I didn't find any explanation about how we integrate yolo with live stream.
Waiting for more videos on yolo👏👏
yup next one will cover coding part