74 - Image Segmentation using U-Net - Part 2 (Defining U-Net in Python using Keras)
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- Опубликовано: 20 сен 2024
- The previous video in this playlist (labeled Part 1) explains U-Net architecture. This video tutorial explains the process of defining U-Net in Python using Keras API.
The code from this video is available at: github.com/bns... 
Наука
I don't have words to explain how much is important your works for software engineers and biomedical engineers. Thank you.
Thank you very much for your kind comments.
Waaaar this is the best explanation in the whole world so far
Great great great... I would say, I took many courses before and that is why I can follow you. But your explanations are done in such way that I can understand the meaning of single details I did not know before or I've just forgotten..
Thanks for sharing your knowledge.!
Great to hear!
Really thank you sir...I am following your videos , they are helping me to do my research workst..... No one in this world is like you to teach to others without any secret. Your are delivering all to everyone very openly... A good hearted person.
One of best videos on unet. Hands down
I loved the sleek implementation of the image normalization
Amazing works ....please keep uploading these pieces of stuff related to Biomedical Imaging!
I will try my best
Thanks very much, I learned a lot from your video, including the other channel ! Pure GOLD !
Great to hear!
These serie is incredible, thank you for your work and time.
My pleasure!
Great video to understand the codding of a U-Net CNN!
Hello Sir!
Words can't explain how amazing work you're doing. Fantastic playlist and so much needed. Kudos!
Also, video no. 75 is not there (Part 3 is missing) Can you please check? Thanks a lot.
Please check my playlist, I definitely see video 75.
@@DigitalSreeni Some of your videos are kept as private for example video no. 72. Maybe if you could just check your playlist from another user ID you'll get to know. Thanks
Definitely educational! Thank you.
the best video with the best explanation. Thumbs Up
Glad you think so!
very good explanation. Thank you for the effort you have put in.
Glad it was helpful!
Goddamn, just came across this gem of a channel! Amazing work!
You just earned a subscriber. Well done!
Thanks for subscribing, I really appreciate it.
I also thinks soO he earned
This is so very helpful. Thank you so much!
Thank you for all the guidance
:D
I understand the U-Net is an extension or it is based on fully convolutional network after reading several papers, and one advantage of FCN is that it can take inputs at any sizes. So I am wondering if it also works for u_net, for example, we just define the depth dimension for 3 (rgb images) and leave the width and height blank. Really great series, helped me a lot. Thanks
awesome video
Thank you Very much for your nice educational tutorial!
You are welcome!
I was wondering to ask if there is any youtube channel the same as yours but in pytorch???!tnx for being this amazing...
This video is gold, I’m new in all of this, but why dropout is equal to 0.1. Plus it vary to 0.2 in other lines. Is there a video that explains it? Thank you so much
x='Thank you'
while DigitalSreeni > university_professors:
print(x)
Hi Sreeni... Your videos are so good and they are crystal clear.
Can you please explain what is the need of padding = 'same' for the first 'u6' during expansion path
With 'same' padding the layer's outputs will have the same dimensions as inputs. It automatically takes care of adding required padding to ensure that the input and output dimensions remain the same.
@@DigitalSreeni Again Thanks a lot sir
Excellent! Please make a video about VAE! Variational Auto Encoder! 🥹
Thank you very much!
Why in Conv2DTranspose kernel_size=(2,2) instead of (3,3) and why do you used stride
Because you are concatenating in addition to up-convolution (Transpose). Please read the original paper, not sure if they explained it in detail. arxiv.org/pdf/1505.04597.pdf
u9 = tf.keras.layers.concatenate([u9, c1], axis=3) why you used axis=3 here?
great video.
Could someone explain why did he pick a 2x2 kernel in the transpose convolution in the upward/decoder path instead of the 3x3 he uses for the regular convolutions?
what if in your mask image you have more than 3 classes ( cow, horse, sheep), how the shape of final output would be?
I appreciate your time and effort to teach us DeepLearning model.
Hey, you mentioned that Conv2D Transpose is the exact opposite of Conv2D, isn't it more like the opposite of max - pooling ?
The opposite of max pooling is something like upsampling, in both cases you are just resizing images. Upsampling uses nearest neighbor based bilinear interpolation for upsampling. Very simple math and faster to execute. Conv2D Transpose is a convolution operation and the kernel is defined by learning during the training process. During this operation the image will be upsampled but based on learning during the training process. Here is some good explanation: towardsdatascience.com/types-of-convolutions-in-deep-learning-717013397f4d
Awesome explanation. But one question, why the dropout is set to 0.2 since c3 above?
its to avoid overfitting
Thanks
From 3 Channels(R,G,B) how can we make 16 feauture maps? as 16 is not divisible by 3
Hello Sir! Firstly I must say your videos are total gem... amazing explanation:) thank you for all the guidance..
Can you please explain me the axis = 3 in line 61 of this video?
axis defines the axis along which you'd like to concatenate. Please change values to 1 or -1 to see the output dimensions. Now have a look at it with a value of 3, this shows the right dimensions of 128x128x32 - the shape of our concatenated dataset.
u9 = tf.keras.layers.concatenate([u9, c1], axis=3)
What is the function of the parameter "axis=3" in the above given line?
Thanks!
Thank you very much.
Amazing videos sir , I am facing the issue , after the model is built the training images are not resized it shows me 0/65 and get the undesired result .Could you please suggest me what is the issue
Hello, as i understood that the concatenation function is for RGB, in case of grayscale am i supposed to concatinate?
Thank you for posting this tutorial. what if I am using x-ray images the number of channels would be 1 right. what else would be changed if it is a gray scale image. please advise.
Amazing video, I followed the code rigorously but somehow I keep getting errors when i try to make the skip connections. I get errors for tf.keras.layers.concatenate([u8,c2]) such as this:
ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 32, 32, 32), (None, 64, 64, 32)]
Please let me know what is wrong. Thank you
For concatenation the dimensions of both arrays need to be the same, except for the last axis along which you are concatenating. In your case the first input has a dimension of 32x32x32 and the second one has 64x64x32. You need to make sure either the first one is 64x64x43 or the second one is 32x32x32. If you’re following my code then please make sure your code matches mine. Omitting one little thing can mess up code badly.
Hi Sreeni,
Thank you for the explanation but I have a query. You have used the loss as Binary Cross Entropy because it is Image Classification problem. But it is an Image Segmentation problem right? So, does that loss still hold good?
Isn’t image segmentation same as classification except at a pixel level? Instead of classifying the entire image, you are classifying every pixel. Still a classification problem.
I have a doubt. Why different activation functions have been used in Convolution layers like in your case 'ReLU' and different one in the outputs i.e. 'Sigmoid'?
And definitely, your videos on U-NET are my savior!
They serve different purposes. This may help: ruclips.net/video/aJToUocPLg4/видео.html
Thank you so much
its so helpful thanks
You're welcome!
Hi Sreeni,
I'd like to know how to configure UNet model for different image resolutions (e.g. 250x250)?
Thanks!
You can try existing architecture and just change the input image size. Print out the model summary to make sure everything seems logical. If not, try modifying parameters. You just need to try and see what works. Normally, with enough experience people can do this exercise in their mind (not me yet!).
thank you for this great demonstration. yet I have two a question why using Relu as an activation function in CNN layer and why change it to sigmoid at the last layer?
The relu activation function is actually to discard all the regions that are unlike a particular filter so that the output feature maps will only contain the feature that is found, not the features that are not similar to the filter. The last layer uses a sigmoid activation function in order to determine the probability of each pixel of the output image belonging to the positive class
Its standard when doing binary classification (2 classes) with a deep learning model we use 'Sigmoid' and if doing multiclass classification then use 'Softmax' at the last layer. Also, the number of neurons on the last layer should be equal to the number of classes. So in the case of two classes number of neurons will be 2 in the last layer and activation will be sigmoid and in case we have 4 classes number of neurons will be 4 and the activation function will be softmax at the last layer. @Bareq Raad
@@syedshahwaizbukhari4720 this is a generative model that will create image not classes that's why I asked this question
Why you don't use the Unet model from segmentarion_models? What is different in both approaches?
You can use Unet from segmentation models but this video is for those who want to learn Unet and understand how to implement it. Also, writing your own code for Unet gives you more freedom in defining the architecture. Unet is just the name for architecture, you can modify encoder and decoder networks to your need and liking.
@@DigitalSreeni Thank you! I had some problems trying to apply an Unet model from segmentation_models due to incompatibilities between libraries. Your example could be a way to solve it. Thank you
Learn something from this videos. I am trying to use your test_image.jpg from the first video of yours and contruct the convolution layers, it has some errors :
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
I change this step :
inputs = tf.keras.layers.Input((image))
s = tf.keras.layers.Lambda(lambda x: x / 255) (inputs)
from :
inputs = tf.keras.layers.Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))
Is it possible doing this?
Need your information about this. Also I want to ask how do you make Capture.jpg as you follow this for Image Segmentation for this video. thank you
Whats the logic for doing transpose convs instead of upsampling layers?
Transpose is the opposite of convolution in autoencoders whereas upsampling is the opposite of pooling. So depending on how you want to engineer the layers you pick the right method.
May I ask what dtype and dimensions the input X and y images should have? I'm using SPC Crossentropy and my model is not learning anything. I would like to check if the training generators produce the correct type of image, but I am not sure what the UNet expects.
You normalize/scale your input images so the dtype will be converted to float anyway. If the model is not learning, try a different loss function or learning rate.
Danke!
Thank you very much for your kind contribution Toni. Please keep watching.
How do I do segmentation for the real time images taken by webcam.
thank you for you're videos very informative. what version of tensorflow you are using in this tutorial, thankyou
did he respond you ?
When I use this exact U-Net (also with model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']), no changes at all) and I fit it to the augmented dataset* from video #177, accuracy increases to well over 0.9, but the resulting model predicts only "0". When I add mean IoU to the metrics, mIoU is going up and down, but stays below 0.2. Loss decreases from 0.028 with approx 0.0005 per epoch. What could possibly be cause such behaviour?
*where I got the dataset, the values were in {0, 255}, so I changed them to uint8 in {0, 1}
I found the issue arises from the "sample weights" parameter of my data loader... Never mind.
There is immense satisfaction in successfully troubleshooting an issue :)
tks for your instruction, please tell me how to apply the training model to a 4k image(or larger) instead of using the same size of training data
You should be able to handle images of different sizes for fully convolutional neural networks. Did you try it on large image and did it fail?
Of course, one way to handle is to divide the image into smaller patches.
getting an error
File "C:\Users\swapna\anaconda3\lib\site-packages\tensorflow\python\keras\utils\generic_utils.py", line 778, in validate_kwargs
raise TypeError(error_message, kwarg)
TypeError: ('Keyword argument not understood:', 'kernal_initializer')
runfile('C:/Users/swapna/OneDrive/Desktop/untitled2.py', wdir='C:/Users/swapna/OneDrive/Desktop')
Traceback (most recent call last):
File "C:\Users\swapna\OneDrive\Desktop\untitled2.py", line 20, in
c1=tf.keras.layers.Conv2D(16,(3,3),activation='relu', kernal_initializer='he_normal',padding='same')(c1)
File "C:\Users\swapna\anaconda3\lib\site-packages\tensorflow\python\keras\layers\convolutional.py", line 646, in __init__
super(Conv2D, self).__init__(
File "C:\Users\swapna\anaconda3\lib\site-packages\tensorflow\python\keras\layers\convolutional.py", line 133, in __init__
super(Conv, self).__init__(
File "C:\Users\swapna\anaconda3\lib\site-packages\tensorflow\python\training\tracking\base.py", line 457, in _method_wrapper
result = method(self, *args, **kwargs)
File "C:\Users\swapna\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 318, in __init__
generic_utils.validate_kwargs(kwargs, allowed_kwargs)
File "C:\Users\swapna\anaconda3\lib\site-packages\tensorflow\python\keras\utils\generic_utils.py", line 778, in validate_kwargs
raise TypeError(error_message, kwarg)
TypeError: ('Keyword argument not understood:', 'kernal_initializer')
Please watch this video to handle the error: ruclips.net/video/syJZxDtLujs/видео.html
How I can get the exact code of this tutorial from the github folder?Can anyone say this?
InternalError: cudaGetDevice() failed. Status: CUDA driver version is insufficient for CUDA runtime version
what is this problem?
You seem to be working on an system with GPU for tensorflow but the CUDA version is not compatible. This can be a pain to resolve as you need to be careful about which CUDA and CuDNN versions aren’t compatible with which tensorflow and how they match the specific version of your GPU and its drivers. In other words, you don’t have GPU setup for tensorflow. Please google search for proper installation. If you just want to use CPU then uninstall tensorflow-gpu and only install tensorflow.
@@DigitalSreeni thank you so much ...all of your video series is awesome
@@DigitalSreeni can you please give me your email? i need some help . my thresholded image is full black
It is a great video. Thanks for sharing knowledge. Sir, how to create a mask images from original images. Please make a video about it. Thanks
This is a common question that people ask me and it is really challenging as there is no easy to use tool. Please sign up for a free APEER.com account and we plan on releasing a tool soon (mid-July 2020). By the way, APEER is an online image analysis platform that is free for academia, non-profits and individuals.
@@DigitalSreeni thanks for information and I will sign up for that
You are amazing
I know :)
Hello,
Thank you for your work and time.
how can I applicate 219-unet_model_with_functions_of_blocks.py to mnist dataset
What is the name of the code file on github for this video. The github link you provided has many files, which one is of this video tutorial?
This video is numbered 74, so please look for code number 74 on GitHub. Here is the direct link: github.com/bnsreenu/python_for_microscopists/blob/master/074-Defining%20U-net%20in%20Python%20using%20Keras.py
Hellow, how make tow GPU NVidia geforce 1080 rtx work together in same time for traning ?
Setting up GPU for Tensorflow can be a bit if challenge. There are a few videos on RUclips and I hope they can help you. I tried to record a video on this topic but there are too many things to check and there are various configurations out there. SO a standard video is impossible. You need to check many sources. May be you have better luck than I.
When I wrote the same code I always get the TypeError: Inputs to a layer should be tensors. Got:
error for the c6 line of the code(which is c6 = keras.layers.Conv2D(128, (3, 3), activation = "relu", kernel_initializer = "he_normal", padding = "same")(u6)
) I couldn't find out the result or the reason from the popular sites. If you know why, pls help me :) thanks for all those videos. It's seem I'm going to learn all details of the field through you..
You seem to be mixing various keras imports. I see at least 2 variations, one where you import from tensorflow.python.keras and the other where you just have keras. Please make sure you follow one process. I recommend using tensofrlow.keras. This may fix your issue.
@@DigitalSreeni thanks for your relevancy, I've solved the problem by using concatenate instead of Concatenate :D they are different methods that are belong to keras. When trying to write code unless looking to original one, that sort errors are unfortunately possible to occur..
que chevere :3
Why the dropouts are different in different Layer?
I've been seeing this in other versions of UNET in tensorflow, but why is the number of kernels used in the output layer 1? The paper said we can use the last layer to represent the number of classes, so in a binary segmentation case why is this 1 and not 2?
For binary classification we are trying to classify our input into either 0 or 1. This means we just have one output, either 0 or 1. For example, if our output is 1 we know it is dog and if it is 0 we know it is cat. There is no other possibility so one output is enough for us to classify. We use sigmoid for binary classification, you can also use softmax which would be same as sigmoid for binary problems. You can also treat it like a multiclass problem where you have two classes and use softmax that outputs probability for each class. If you convert the probability to classification you’ll get same result as you would with binary classification. I hope this clarifies your doubt.
Python for Microscopists by Sreeni
That does help, but with a 0 or 1 is still 2 classes. Could you provide an example of when we would have 2 instead of 1 in the layer? I started out with Pytorch and this video really did a good job explaining why it’s 2. m.ruclips.net/video/u1loyDCoGbE/видео.html
Is what you're saying that if we set the output to 2 filters, so 2 classes, and use softmax, then it is the same as you did here with 1 filter and sigmoid?
0 or 1 output is not two classes, it may be counterintuitive. Think of the output layer as giving only one output. That output would be either 0 or 1 which makes it binary.
Yes, exactly.
why not use data augmentation rescaling to get the floating pixel value?
You can perform data augmentation and other tricks to enhance the training data if you want. I am not sure why you would use it as a process to create floating values.
@@DigitalSreeni there is this rescaling option in keras under data augmentation in keras. Just asking is that is a way of doing this rescaling?
can you give me the link of video that is for setting the GPU
Video 72 and U-NET 3rd part (video 75) are missing
Thanks for letting me know. I removed video 72 as it contained information that may not help all viewers. I seem to have missed adding video 75 to the playlist, it is back now.
excuse me, i want to ask you, i'm a new in a ML.
i want to implement this unet for lung segmentation, am i need a lung mask for do that?
can you suggest me a step for do that.
thanks
Please go through my videos about U-net and segmentation to get a good understanding of what it is all about. I covered many topics using U-net and you will find answers to many of your questions. And yes, you need to provide ground truth for supervised deep learning and for semantic segmentation it is done via masks representing various labels. You can label your images many ways, the one I use is from www.apeer.com because this what we do at work.
@@DigitalSreeni ok, thanks for your answer, I'm really happy for watching your videos.
Hi, After segmentation how to do feature extraction and classify those features?
Not sure what you mean. Why would you do feature extraction after segmentation; in fact feature extraction is done to facilitate segmentation. Do you mean object measurement after segmentation?
@@DigitalSreeni If we didn't do feature extraction then how can we classify in the heart vessel detection dataset that it is normal or abnormal?
Can we use unet architecture for image to image translation
Yes, of course. But I think GANs are better for domain transformation type applications. I need to find time to record videos on GANs. Hopefully sometime soon.
Waiting for gan. Thanks for the response
Hi fantastic video explaning sematic segmentation and btw are you a telugu guy
Yes, spent the first 21 years of my life in Hyderabad, love the city.
@@DigitalSreeni And now where are you?
San Francisco Bay Area.
@@DigitalSreeni In which company are you working? And this is d last question
Please check my LinkedIn profile, the link is on the channel main page.
Hello sir.....which IDE have you used to execute these codes?
Can I use pycharm or vscode for this
You can use any IDE you want, the code doesn't care. So please pick the one you're comfortable with.
anyone get any error while import the segmentation library ???
Image masks can be created using which tool?
sensarea
sir , i have modified the input size and regarding changes in c1, c2 etc but i have got error Lambda object has no attribute shape.why it is occur.
Does the image resolution matters here? I am getting error about different matching shapes when trying to put my resolution instead of yours.
Hey, can you help me here? Please
ValueError: Input arrays should have the same number of samples as target arrays. Found 670 input samples and 128 target samples.
My code is exactly the same as yours and can't understand why this is happening
How to define if unet multiclass? I dont know how to do it somebody please help me :)
is this program applicable to CPU version too
Yes, of course.
How do we apply Batch Normalization, if we want to apply?
At what stage, should we apply? And how to use that?
which library is better to implement the network : Keras or PyTorch ?
Doesn't matter, it depends on your comfort. Keras is usually easy for most people, especially beginners.
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: ruclips.net/video/9dTfUwnL6zY/видео.html (especially suited for histopathological whole slide images).
Hello, how can I contact you? I am a master student from Iraq.... I work in semantic segmentation programming using u_net, the cityscapes database, but the program is not implemented by spider... How can you help me?
Sorry, I cannot help with personal projects. I wish I had that kind of time but unfortunately I do not. You can try posting your specific questions on my Discord server and see if someone can answer. discord.gg/QFe9dsEn8p