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when will I getYOLO V5 video

Please upload others yolo model

@MLForNerds Why do running_mean and running_var continue to be updated during training, even after the dataset has been processed for an epoch?

can you please share ppt/slides you used in this video i've my presentation on v2 next week

This is as close to perfect as it's going to get for explaining the core of YOLO. Thank you

Very informative, waiting v5,v6,v7 also and more videos

Please continue this series... they are underrated

03:40 Iam confused, how the cell would know that the ground truth object's center falls inside it both in training and inference part. And after that , how the cell predicts the x, y, w, h, coordinates (anchors) as we don't know the size or shape of the object. Cause after training, the CNN would be able to extract the object features. Hence Objectness scores and class probabilities for each cell are understandable.

As an interested layman, I am reading a book about ML but with only the book at hand it is very difficult to grasp how a neural network is working in 'real live'. Thank you very much for the clarity of your video. Makes thing much more easier to understand. This is of great value!

Thanks for the detailed explanation

Incredible explanation, thank you very much

Sir you deserve more subscribers

If you are trying to land a job what matters is what companies are using, not research papers or kaggle competitions.

very informative video and great explanation. Could you continue explain all versions of YOLO?

Thank you for this wonderful video. I have one question. YOLOv2 decides the ratios for the anchor boxes using the GT(ground truth) dataset. So does that mean it cannot be used(retrained) with the dataset that doesn't have GTs?

Greatest lecture... I have ever seen on youtube...Thank you so much..

amazing it is...thanks

2 min silence for those who can't find this video ! Best video for yolo i read it paper,watch video , read article and i was confused like hell in loss fn and bounding box now its clear thank you so much i recommed to everyone who is planning to study deep learning ❤♥

Thanks for your amazing YOLO series videos!

Is anchor boxes are created based on grid.. means the center of anchor will be in the selected grid?

The number of prediction for each grid is not equal to the number of classes. Its depend upon the number of anchor boxes and in the paper they have taken the 5 anchor boxes for each grid cell. So maximum number of prediction can yolov2 is 13*13*5.

Please explain more yolo versions from yolov5

class confidence is not conditional probability, the individual probabilities are conditional P(class_i | Object) and when you multiply with C_1 aka Pr(object), we get non conditional probabilities i.e. only Pr(Class_i)

for tx ty they are relative to grid cell why we are not multiplying with 64 like in yolov1

This implementation has overflow-error with very small or large values for x

bro start making videos once again, your explanataions are very good

Thanks a lot for this video, this was really helpful

Thanks a lot for your video, this helped me a lot to understand its working

Also, how is it possible that beta/gamma make a non-symmetric, non-normal distribution like in your picture? It can only give a different normal distribution that may not have mean 0 and std 1. But it's a linear transformation of a std normal, so it must still be normally distributed. Right?

I'm a little confused...you say at around 10:21 that in each mini batch we normalize the x's, which usually denotes the inputs and this doesn't seem like what I think of as batch normalization. But then you go on to say in the visual explanation afterwards what I expected and what I think is correct which is that we normalize the Zs in layers after the input layer before sending them to the activations. So, to be be clear: we don't normalize the batch inputs themselves, just the Zs, right? Or is it both? By the way, this is excellent material--it's just that this one point was confusing me a little

woah, i am happy i found this!

This is amazing could you do a transformer series!

Thanks

Glad! i found this legendary explanation

Best

Bro ❤ that's so better than 200 usdt lectures

hiii will u post yolo v5 ? this is the best playlist

thank you! clear and direct.

Thank you very much for the best explaination of yolo papers on youtube. I have a question of loss calculation on multiscale training. This affects the number of output grid (WxHXS) used in loss calculation when input image(WxH) size changes. How does the loss calculation maintain consistency for this training scheme?

could you please mention source of the mathematical explanations it would be great help.

The best explanations of YOLO on TH-cam. Period. Thank you!🙏

really great job sir! waiting for more lately versions

You rock!!! It was very detailed. Clearly, you have out a lot of work into this. Thank you so much🙏🙏🙏🙏🙏🙏

Just wow!

The best explanation for this concept, kindly keep making such content. Thanks a lot.

thanks for giving us perfect video, love you bro

thanks for helping us a lot in learning, truly appreciate your work

very detailed explanation, Thanks for making it more clear. I believe i didn't find any such video with the way you explained the things in deep. I have a doubt when you said total loss = obj loss+no obj loss, In the example you considered only 2 grid cells has an object which means obj loss is calculated for those 2 grid cells and remaining 47 grid cells falls under no obj loss right?

Underrated. Keep going man!

thank you i could have nerver learn and understand any better then your video