Thank you for the video. I think the best video for basic levels / intermediate levels.
@shafagh_projects Жыл бұрын
I am speechless. your tutorials are beyond the amazing. thank you so much for all you have done!
@DigitalSreeni Жыл бұрын
Glad you like them!
@boy11903 жыл бұрын
I wish youtube give us an option of liking video after every minute, this idea came in my mind for the first time in this video, I really want to give this video a like on every small bit of concept. Because it is explained so well. Respect Sir.
@zeeshankhanyousafzai52292 жыл бұрын
I can not express my wishes for you in the words. You are more than the best. Thank you so much.
@DigitalSreeni2 жыл бұрын
You are most welcome
@iamadarshmohanty3 жыл бұрын
the best explanation I found on the internet. Thank you
@Rocky-xb3vc4 жыл бұрын
This is the first video I'm watching on this channel, and I need to say huge THANK YOU. You helped me connect so many dots that were all over the place in understanding this. Amazing.
@DigitalSreeni4 жыл бұрын
Thank you very much for your kind feedback. I hope you’ll watch other videos on my channel and find them useful too.
@Rocky-xb3vc4 жыл бұрын
@@DigitalSreeni Of course, I've already watched the full course and the next thing is time series forecasting. Thanks for your reply and everything you do!
@NeverTrustTheMarmot2 жыл бұрын
Pick up line for data scientists: Why is U-Net architecture so beautiful? Cause it looks like U
@adityagoel2372 жыл бұрын
14:25 In upsampling (before adding C4), why the 8*8*256 got transformed to 16*16*128 ? Why not 16*16*256 ?
@ramshaqayyum396722 күн бұрын
I have the same doubt.
@brunospfc85112 жыл бұрын
Thanks Professor, there's so much knowledge in you channel, i'll need months to go through as it seems it's right in the deep learning area i want to focus, as an Computer Engineering going throught Veterinary course, blood sample analysis may be my final project, thanks from Brazil
@DigitalSreeni2 жыл бұрын
I am sure you'll benefit from my tutorials if your goal is to analyze images by writing code in python.
@tamerius14 жыл бұрын
Why does the feature space and thus depth increase as we go down? Is this a design choice or a consequence? It's confusing for me that each first convolutional operation increases the depth and the second one which seems identical, does not.
@codebeings3 жыл бұрын
13:54 Do check, second last layer in the decoder side have wrong connections !
@kunalsuri83163 жыл бұрын
How is it wrong?
@codebeings3 жыл бұрын
@@kunalsuri8316 In the second last layer of decoder (corresponding to P1), its input to the last layer of decoder is incorrect. Just check the original paper, one can easily notice it.
@Tomerkad Жыл бұрын
thank you. can you please explain what does it mean to add C4 to U6 in the first Upsample step?
@Julian-ri9od2 жыл бұрын
Is there a reason why always two convolutions are applied after the max pooling step? Is it a convention to use always two?
@DigitalSreeni2 жыл бұрын
No reason. It may appear that 2 convolutions are added after maxpool on some architectures but that is not the general case.
@siddharthmagadum162 жыл бұрын
5:12 . which architecture would be good for cassava leaf disease detection dataset?
@lazotteliquide Жыл бұрын
Incredible that someone as dedicated as you gave accss to such great knowledge. Thanks you, you help create better sciences
@blueicer1013 ай бұрын
It's actually crazy how people just make tutorials on this knowledge stuff for free.
@张衡-m4m Жыл бұрын
thank you, professor, helps a lot in my understanding of deep learning.
@leo467283 жыл бұрын
17:56 Does the model need to be trained after compiling?
@DigitalSreeni3 жыл бұрын
Compiling just defines the model, you need to train the model on real data to update the weights and customize it for a specific job to be done, for example identify cats and dogs.
@leo467283 жыл бұрын
@@DigitalSreeni ok thanks
@Vibertex3 жыл бұрын
Great Video! Really helped me understand U-Nets for my own use!
@DigitalSreeni3 жыл бұрын
Great to hear!
@anishjain36634 жыл бұрын
Sir i am doing image segmentation with coco like dataset sir already see yours tutorials but still not able to implement
@mincasurong6 ай бұрын
Thanks for your amazing presentation!
@haythammagdi3956 Жыл бұрын
Hi every one. It is really amazing video on U-Net. But waht about U2-Net? is it better?
@BiswajitJena_chandu4 жыл бұрын
Sir, please do a video for segmentation of BRATS dataset
@ahmad38233 ай бұрын
from 3 channels and applying 96 filters to each channel, shouldn't we get 288 channels? Also, in the max-pooling step, from 96 channels, how do we have 256 channels? shouldn't we still have 96 channels? Sorry, if these questions seem very basic but I am new to these things! Thank you!
@bhavanigarrepally41642 жыл бұрын
Can you give the implementation for unsupervised semantic segmentation also
@-arabsoccer15534 жыл бұрын
Thanks for your video,but i have question regarding the U-net and i hope that you can answer me from my understanding that the u-net is ended by image of the same input size ?but how we can predict the class of each pixel. i understand classification problem that it the last convolution is following by flatting and fully-connected layer so the number of n-classes as outputs ,but i don't understand how we get the result in segmentation
@DigitalSreeni4 жыл бұрын
The convolution pooling operations (down sampling) understands the 'what' information in the image but has no information on the 'where' aspect which is required for semantic segmentation (pixel level). In order to get the 'where' information Unet uses upsampling (decoder), converting low resolution to high resolution. Please read the original paper for more information: arxiv.org/abs/1505.04597
@nailashah69183 жыл бұрын
very good lecture just one thing I am unable to understand about feature space or dimension?plz reply with answer thanks
@DigitalSreeni3 жыл бұрын
Not sure where your confusion is.... I am referring to the filtered results (after convolutional filtering) as feature space. This is where you will have multiple responses for every input image and these responses contain the information about features in the image.
@nailashah69183 жыл бұрын
I wanted to ask about feature space that was 64 in start then 128 in 2nd block of unet 64 means 64 output filtered results? is that true? or we can say 64 filters were applied, then 128 filters and so on ...?
@matthewchung744 жыл бұрын
Thank you for this very helpful video. In the unet diagram, there are 3 output features, but your implementation only has one. I'm confused as to why?
@tomrob1234 жыл бұрын
As im just starting to dig into this field im not quite sure but my suggestion would be that the output has to be a segmented image. Segmented images have value 1 for the segmented part and value 0 for the remaining non segmented part of the picture. Usually when using segmentation grey values are considered. And for grey values only one channel is needed.
@Shadow-pn2us5 жыл бұрын
still confused with the concatnation operation how it works, such as adding 16x16x128 featuremap with upsampled 8x8x256, the dimension is different
@DigitalSreeni5 жыл бұрын
You’ll be concatenating data with same dimensions, not different dimensions. Please have a second look at the graphic describing the architecture, the two layers fused together showing dimensions are being concatenating to form a dataset with combined dimension.
@RAZZKIRAN2 жыл бұрын
input size for U-nET?
@saifeddinebarkia71863 жыл бұрын
Thanks for the video,so is it transposed convolution or up-sampling for the expansive path because they are 2 different things.
@DigitalSreeni3 жыл бұрын
It can be either. Please watch the following video if interested in learning about the differences between the two. But, you can use either as the idea is to get back to the large resolution image from a smaller size. kzbin.info/www/bejne/nH7apZxsr6uWj7s
@Irfankhan-jt9ug3 жыл бұрын
Great work......which tool creates Image masks?
@chitti11203 жыл бұрын
can someone tell me and give examples of why the u-net architecture uses the 'copy and crop' for every block?
@ahtishamulhaq14152 жыл бұрын
I can't Find Code Please Tell the name of folder
@icomment46923 жыл бұрын
What implication do the cross-links have for backpropagation in the U-net architecture?
@kebabsharif96273 жыл бұрын
Can you make a video in which your code detect the orientation of page from a photography of the page , suppose to the page is up-side down or 90° let /right rotated.
@joshizic6917 Жыл бұрын
Hi sir i was wondering if you could help to train my model i am trying to create a dataset where only the element of interest is visible and the rest is blacked out with transparent background , will this be great or i should create a binary mask by coloring the element of interest in white and keeping the background white
@talha_anwar4 жыл бұрын
Thanks first of all. Can you provide the image you have used, the architecture image?
@DigitalSreeni4 жыл бұрын
You can search for U-net on Google. I did the same and created my own, to make sure I do not infringe on copyright.
@sourabhsingh48954 жыл бұрын
@@DigitalSreeni sir you are great sir it would be a great help if you could upload a video on semantic segmentation using double-UNET model
@ioannisgkan89303 жыл бұрын
Great explanation SIR You made us simple
@DigitalSreeni3 жыл бұрын
Glad to hear that
@ramanjaneyuluthanniru14284 жыл бұрын
Well explained....sreeni you have amazing teaching skills...your explanation pretty good. i watched more and more videos in youtube...you also one of the best person thanks for sharing information
@DigitalSreeni4 жыл бұрын
Thank you so much 🙂
@varungoel1854 жыл бұрын
Nice video, thanks! One question - this architecture is for semantic segmentation right? How would the final layer (or layers) differ for the instance segmentation, wherein the output would be bounding boxes or co-ordinates of the instances?
@DigitalSreeni4 жыл бұрын
Instance segmentation requires different architecture, you cannot swap the final layer to convert them from one to another application. I only wish life were that easy!!!
@ahmedhafez37583 жыл бұрын
I want to make a 3D medical image segmentation , can you tell me how to start, I want the input to be .obj file and the output to be either .dcm files ( for each segment ) or .obj files
@NS-te8jx2 жыл бұрын
do you have slides for all these videos?
@andresbergsneider66444 жыл бұрын
Thanks for sharing! Very well presented and super informative. Saving this video
@nickpgr103 жыл бұрын
@14.11.. can anyone please explain how size changes from 8*8*256 to 16*16*128 due to up sampling??. why number of channels get reduced in this step??
@zhenxingzhang64293 жыл бұрын
If you checkout the part2 of this video, you can see that it uses Conv2DTranspose (transpose convolutions) for upsampling instead of just simply UpSampling2D (repeat the value to match the desired dimosions), because the filter number is set to 128, so we end up with 8*8*256 -> 16*16*128. Check this for more details: www.jeremyjordan.me/semantic-segmentation/#upsampling
@isaaciwediba33805 ай бұрын
You are doing a great work, I have learnt a lot from you. could you please treat segmentation using DeepLab? thank you.
@sarahs.33954 жыл бұрын
Good explanation, thank you.
@ariouathanane Жыл бұрын
Hello, i have a rgb masks, it's possible to do the image segmentation? thanks in advance
@DigitalSreeni Жыл бұрын
Yes. I have done that here. kzbin.info/www/bejne/oKe9nmuIeqtlgbs
@mqfk31519854 жыл бұрын
As usual! Amazing tutorial. I just want to confirm, in the training phase, all images have to be of the same shape (width, height and depth), right? what if my training data varies in shape? Do I need to resize the images? Also, I Will be really thankful if you can give a tutorial on Mask RCNN. It's also a very good algorithm that can be used for semantic segmentation. Thanks a lot for your time.
@manishsharma22114 жыл бұрын
Yes. Always apply transformation on image ( like resizing and rotation etc)
@mqfk31519854 жыл бұрын
I see, thanks for the reply. Image rotation will be performed for data augmentation. but regarding the image resizing, I think it's a requirement by the algorithm.
@manishsharma22114 жыл бұрын
@@mqfk3151985 Yes , there is never that you might find images of all same size. Unless you go for normal competation So better resize :)
@DigitalSreeni4 жыл бұрын
You will represent your data as numpy array so you need all images to be of same size. Yes, it is customary to resize images to a predefined shape in machine learning. I will consider making Mask-RCNN videos.
@doraadventurer99334 жыл бұрын
thank you for your sharing, however, do you have the training part?
@DigitalSreeni4 жыл бұрын
Please keep watching videos on this playlist, I have training and segmentation part covered.
@4MyStudents2 жыл бұрын
basically, ReLU is used to prevent overfilling to maintain non-linearity
@govtjobs7063 Жыл бұрын
Sir is this unet architecture for multiclass segmentation or binary segmentation?? Kindly response
@DigitalSreeni Жыл бұрын
This is binary. I got many other videos on multiclass.
@govtjobs7063 Жыл бұрын
@@DigitalSreeni ok sir ...Thank you for your response...sir i have one more question ...when we are combining t2,flair and t1ce...do we call that combined image a single channel image or 3 channel image...please sir reply
@carolinchensita Жыл бұрын
Thank you very much for this explanation. I have one question, could I use this same method on an RGB image? Or does it have to be grayscale? Thanks!
@rohanaggarwal8718 Жыл бұрын
This is a late reply but yes, you have to expand your thinking... You can't assume just because someone made a tutorial this is what i have to do. Ask yourself these questions instead of trying to get help, What is a grayscale image? (1 is white, 0 is black, in between is gray) Can I apply this concept to RGB? (Three color channels, each same principle) How does my code change, (Input shoudl be three, maybe I need to flatten differently), etc. Good luck learning!
@mager84603 жыл бұрын
Could someone explain why on upsampling the number of the feature maps reduce to the half?
@DigitalSreeni3 жыл бұрын
Upsampling is not reducing the feature maps in half, it is expanding dimensions by 2 times as upsampling is like the opposite of maxpooling. The feature maps are reduced by half because that is what we defined in our network as part of convolution operation. The number of features has nothing to do with upsampling.
@anishjain36634 жыл бұрын
sir how to use this 3d images and what is 3d image can you please can you make a video on that
@DigitalSreeni4 жыл бұрын
I will try to do 3D image processing some day.
@aishstha66692 жыл бұрын
@@DigitalSreeni do you va e vid on 3D ?
@DAYYAN2946 ай бұрын
Great job by you sir salute to u❤
@BareqRaad3 жыл бұрын
Great demonstration thank you so much
@victorcahui7323 жыл бұрын
Thank you for your explanation.
@snehalwagh22832 жыл бұрын
Question: What happens if it is 128X128X1 ? will it still become 128X128X16 ?
@tonihullzer16113 жыл бұрын
First of all thx for your work here on KZbin, when I'm done with your series I will definitely support you. One question here: I thought that in the upward path you do add the upsampled features and the corresponding ones from the contracting path, but in your code you have concat?
@MrAmgadHasan Жыл бұрын
He's concatenating and then uses a convolution layer. This has a similar effect to adding since the convolution operation adds the results after multiplication
@TheedonCritic2 жыл бұрын
Awesome! I'm trying to use GAN for augmenting my images and masks which I will use as input to my semantic segmentation models, but I can't find any tutorials online. Most of them are for classification datasets, any advice, please?
@azamatjonmalikov95533 жыл бұрын
Amazing content as usual, well done :)
@mimo-wx9mc4 жыл бұрын
why the first parameters don't work very well and how we can determine the best parameters
@DigitalSreeni4 жыл бұрын
No sure what you mean by parameters. If you are asking about hyper parameters that go into defining your network then it is not an easy answer. People are still researching the effect of parameters for various applications.
@shreearmygirl98783 жыл бұрын
Hello sir, plcan u provide the links of videos for creating our own dataset from scratch fro satellite images, pl sir.. its very important.I hope you will...
@DigitalSreeni3 жыл бұрын
You just need to annotate your images using any of the image annotation tools out there. I use www.apeer.com as that is what our team does at work.
@hanfeng324 жыл бұрын
thank you, this video is the best
@nourhanelsayedelaraby42713 жыл бұрын
first of all thank u for the great explanation and wanted to ask u about the slides if they are available
@DigitalSreeni3 жыл бұрын
Sorry, I wasn't very planned with my presentation slides so unfortunately I cannot share them. Also, I often use images and content from Google searching that come with copyright. I cannot legally distribute them.
@MrChudhi2 жыл бұрын
Hi, Sreeni, Nice explanation and I managed to clear my doubts. Thanks. Do you have any videos on image segmentation with pertained models.
@ExV61204 жыл бұрын
I still don't get it, what exactly is the 16, 32, 64, 128, 256 that being called features in the next two layers each?
@DigitalSreeni4 жыл бұрын
Think of it as applying 16 different digital filters and then 32 and the 64 and so on.... Therefore, if you take a single image of size 256x256 and apply 16 different filters on it you will end up with 16 responses from this single image --> 256x256x16 data points.
@andresbergsneider66443 жыл бұрын
@@DigitalSreeni What is the design principle behind this filters, any rules of thumb? Are they generated at random? Or are this manually configured? Thanks again for sharing this video!
@kethusnehalatha60914 жыл бұрын
For better results what changes we have to do in the u net sir ???
@DigitalSreeni4 жыл бұрын
Many things. For example you can try replacing generic encoder (down sampling) part with something sophisticated like efficientnet.
@qw43163 жыл бұрын
Hello sir is there any possible use U net to denoise ?
@DigitalSreeni3 жыл бұрын
I am curious why you're asking this question. If you are an ML researcher trying to design new models then you can try U-net approach and see if it works or requires any modification. If you are trying to find the right tool for a given job, I do not recommend experimenting with U-Net as it is not designed for denoising. It is designed to perform image segmentation. For denoising scientific images, you may want to look into Noise2Noise or Noise2Void techniques.
@qw43163 жыл бұрын
@@DigitalSreeni thanks ,Sir!
@qw43163 жыл бұрын
@@DigitalSreeni yup , I found most of the method of denoise are applied to image , but my application is used on data of structure 1X256 .mat so this is the point confused me . I
@qw43163 жыл бұрын
@@DigitalSreeni yup actually I followed you vedio ,I think I can command the U-net design , but for my data ,I am confused . Could you help to have a look my data and give me a suggestion ,sir ?
@alessioandreoli21454 жыл бұрын
Hi!which is the best segmentation technique I can use in python for cells image counting/object detection/size definition?
@DigitalSreeni4 жыл бұрын
The best method is always traditional approaches of using histogram for thresholding and then some operators like open/close to clean up. If that is not possible then the next best option is to use traditional machine learning (extract features and then Random Forest or SVM). I covered that topic on my channel. FInally, if you have the luxury of 1000s of labeled images then use deep learning.
@alessioandreoli21454 жыл бұрын
@@DigitalSreeni , please let me one more question. My purpose is to avoid manual settings to use macros or python over big amount of images taken at cells on a big microscale range. Any suggestions there? Have you any reference for deep learning?
@pearlmarysamuel48094 жыл бұрын
How much memory does the original unet require?
@DigitalSreeni4 жыл бұрын
Not a simple answer. Here is some good reading material on this topic. imatge-upc.github.io/telecombcn-2016-dlcv/slides/D2L1-memory.pdf
@vikaskarade55854 жыл бұрын
Amazing Lecture. You can also create one on UNET++ and attention UNET. I was looking for these topics and I wish you had one on these topics... :)
@DigitalSreeni4 жыл бұрын
Great suggestion!
@منةالرحمن4 жыл бұрын
Thank you again Would you please tell me, is it possible to use data augmentation befor semantic segmentation an how to apply same function on both image and mask
@NH-gl8do4 жыл бұрын
Very excellent explanation
@DigitalSreeni4 жыл бұрын
Glad it was helpful!
@tonix19933 жыл бұрын
Very helpful video thank you!
@DigitalSreeni3 жыл бұрын
Glad it was helpful!
@zeeshanahmed39974 жыл бұрын
hello! I want ask something, can I train my unet model with the input training images having only single channel? like (img_height, img_width, 1) or (img_height, img_width) ?
@DigitalSreeni4 жыл бұрын
Yes. Please watch my other videos on U-net. Every network expects certain dimensions and you can reshape your arrays to fit those dimensions. For example if you have grey images with dimensions (x, y, 1) and if the network takes 3 channels then just copy the image 2 more times to convert to (x, y, 3).
@akainu36683 жыл бұрын
hi can you also create one tutorial on unet based segmentation for isbi 2012 data set or brats data set ?
@DigitalSreeni3 жыл бұрын
I already did Brats. Please check my videos 231 to 234.
@MadharapuKavyaP2 жыл бұрын
Hello sir, can u please make a video on brain tumor segmentation using u net architecture integrated with correlation model and fusion mechanism.
@eastlee90903 жыл бұрын
Hi, Sir. Is chapter 72 missing?
@DigitalSreeni3 жыл бұрын
Yes, it is missing because it was about getting system ready for GPU and the process does not make sense any more with new TensorFlow. I am planning on recording a new video on the topic.
@xianglongchen30882 жыл бұрын
Is this keras code?
@muhammadzubairbaloch32242 жыл бұрын
Depth estimation using neural network. please make the lecture
@mstozdag4 жыл бұрын
Hello, great content! Where is the code for U-Net? Can u post the link here pls?
@DigitalSreeni4 жыл бұрын
github.com/bnsreenu/python_for_microscopists
@mohamedelbshier28182 жыл бұрын
Thank you and Respect Sir
@DigitalSreeni2 жыл бұрын
You are welcome.
@qazisamiullahkhan14972 жыл бұрын
Please sir make a video on sliver07 dataset
@deepMOOC4 жыл бұрын
Thank you,but how can I get the code
@DigitalSreeni4 жыл бұрын
You can get the code from my GitHub page. The link is provided under my channel description.
@temurochilov3 жыл бұрын
Thank you very informative tutorial
@DigitalSreeni3 жыл бұрын
Glad it was helpful!
@pratheeeeeesh48394 жыл бұрын
classy explanation!
@maciejkolodziejczyk41364 жыл бұрын
Many thanks, well done !
@DigitalSreeni4 жыл бұрын
Many thanks!
@poopenfarten4222 Жыл бұрын
what are the numbers above the layers, for eg in the first layer 16 is written above it, what does it signify could someone please explain
@veeraraghavareddy85003 жыл бұрын
Can Anyone Tell UNET Fullform???
@pavankumarakula22683 жыл бұрын
As @DigitalSreeni explained in the video, the name is because of the shape of the network. So there is no acronym for it. Also in the original paper the authors haven't mentioned any full form for it.
@HenrikSahlinPettersen3 жыл бұрын
For a tutorial on how to do deep learning based segmentation without the need to write any code using only open-source free software, we have recently published an arXiv preprint of this pipeline with a tutorial video here: kzbin.info/www/bejne/b5W3l4ito7FpsLs (especially suited for histopathological whole slide images).
@VLM2343 жыл бұрын
Great explanation....Please keep on posting such high-value videos..... If we have less data, then we should go for Transfer learning or Machine Learning approach??
@efremyohannes23344 жыл бұрын
Thank you sir, very nice video.
@DigitalSreeni4 жыл бұрын
Most welcome
@CristhianSanchez4 жыл бұрын
Great explanation!
@DigitalSreeni4 жыл бұрын
Glad you think so!
@shanisssss59064 жыл бұрын
Fantastic video!
@ApPillon2 жыл бұрын
Thanks bro. Cheers!
@prashant0074203 жыл бұрын
Thank you for the video. this is best video. My only request please make same type video for Mask R CNN for image segmentation i have a project on this i have to submit in this week but Mask R cnn is confusing. so please help me on that.