Відео

9:02 i have doubt: how are the number of features in the LHS and RHS matching? LHS = w *h*c. RHS = (w/2)*(h/2)*(2*c). Thus RHS = 2*LHS

Thanks, this is really good. One thing that would be helpful is if the example was itself convoluted like the algorithm, to make easier to visualise the algo.

U NETS RULEEEEEEEEEEEEE

The best explanation that I've ever seen. You answer all the questions I've had. kudos to you

whould you please make a presentation on 3D Unet . that would be really appreciated

Man i like you ! . you are the best ! how you simplify thing and how you are careful to deliver the idea perfectly >> please keep this great presentation up >>

I LIKEED THE ANIMATIONS AND YOUR PTESENTING STYLE IN THE VIDEO. THANKS.

I found this while looking up UNet ELI5... 😭😭

nice effort, but the sound of music is distracting.

at time 2:09 image 1- image2 = image 3 does not imply image1 + image 3 = image 2

I’m interested at multiclass problems (recognising bike, human AND house). Also what would you choose instead of confusion matrix?

everyone is praising the video, maybe it's just me but i really didn't understand what the residual connection hopes to achieve? and how does it do that? didn't make it clear.

7:53 Why is there a need to preserve the time complexity per layer?

Make a video on I-JEPA

great vide mate , would love to see more brilliant stuff like this❤❤

Very well explained

Hey just show this first video from your channel and immediately subscribed to your:) Great explaination with visuals

best explainer!! great video, I had an "aaaaááaaa" moment at 8:05

Very useful and great explanation.

This is one of the best videos ive ever come across on youtube ngl, GG

6:38 this is the part that really made me understand, thank you

Love it bro!!

You didn't explain how the skip connections are connected across. What is the data that's transferred and how is it incorporated into the output half of the U-Net?

Thanks a whole big ton!

so simple and straightforward

Love the Music

bestvideo for understanding U-net model

It was really the most simplified and to the point video I watched on this topic. Great work!!

You are very funny!

Great summary, Great thanks

If you want to just use the Decoder how would you do it?

great explanation thanks!

I feel like this is more a description to experts than an actual explanation of how and why it works. Questions I'm left with: What is the purpose of downsampling/upsampling (I'm guessing performance?) How is segmentation actually done by the u-net? How is feature extraction actually done? What are max pooling layers? What does "channel doubling" mean, and what does it achieve? How does the encoder know "these are the pixels where the bike is"? Why is it beneficial to connect the encoder features to the decoder features at each step, versus in the last step? How does unet achieve anything other than downscaling/upscaling performance efficiency? Where are the actual operations to derive features? How is u-net specifically applied for various use cases like diffusion? What does diffusion add or change, for example.

Nice explanation

bro why did u stop making videos i need you lmao (its a painful lmao.)

dude thankssssss i thought this was another one of these things thatll take me 2 hours of youtube to *not* understand, but u saved me

Clearly explained. What caused my consfusion in the first place is, in the graphic in the original paper, why does the segmentation mask not have the same dimensionality than the input image?

The best ever video you can get on Unet explaination

Great video champ

Fucking fabulous

If anyone wonders how to concatenate the features if they don't match the size... they crop it.

Thank you very much bro...

I clicked on thumb down for wasting one minute of my precious time in the intro. Get to the F point !!

TIGHT TIGHT TIGHT

nice video, very helpful

thanks for the video, I am trying to use U-net for anomaly detection in time series and your video gave me the idea.

tried just everything but getting 38% hamming score accuracy on my multilabel classificastion of 24000 dataset into 26 labels, please suggest something

You might not find my comment since the video is too old, but man I just want to thank you for this video. I am a student who has always been interested in computer graphics and related fields like game engines, physical rendering, ray tracing, etc, and jst didnt get the ML/AI hype everyone was on the past 2 years. I only ever managed to study ML basics for 2 weeks before I left it for good. But recently I got in a team where my friends were working on CNN based projects, and that made me learn about many basics about NNs and DL. This explaination for Unet seals the deal for me, and I will strive to work on integrating my two interests into one and hopefully create something I love.

Me seeing the video at 1.5x 😂😅

cool info, thanks rupert ai