I don't have words to explain how much is important your works for software engineers and biomedical engineers. Thank you.

Waaaar this is the best explanation in the whole world so far

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.

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.!

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!

Thanks very much, I learned a lot from your video, including the other channel ! Pure GOLD !

Great video to understand the codding of a U-Net CNN!

These serie is incredible, thank you for your work and time.

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.

Definitely educational! Thank you.

the best video with the best explanation. Thumbs Up

very good explanation. Thank you for the effort you have put in.

Goddamn, just came across this gem of a channel! Amazing work!

This is so very helpful. Thank you so much!

You just earned a subscriber. Well done!

I was wondering to ask if there is any youtube channel the same as yours but in pytorch???!tnx for being this amazing...

awesome video

Thank you for all the guidance :D

Why in Conv2DTranspose kernel_size=(2,2) instead of (3,3) and why do you used stride

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

Thank you Very much for your nice educational tutorial!

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

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

Hello, as i understood that the concatenation function is for RGB, in case of grayscale am i supposed to concatinate?

Thank you very much!

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?

Hey, you mentioned that Conv2D Transpose is the exact opposite of Conv2D, isn't it more like the opposite of max - pooling ?

How do I do segmentation for the real time images taken by webcam.

Excellent! Please make a video about VAE! Variational Auto Encoder! 🥹

u9 = tf.keras.layers.concatenate([u9, c1], axis=3) What is the function of the parameter "axis=3" in the above given line?

Hi Sreeni, I'd like to know how to configure UNet model for different image resolutions (e.g. 250x250)? Thanks!

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?

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.

Why you don't use the Unet model from segmentarion_models? What is different in both approaches?

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?

Awesome explanation. But one question, why the dropout is set to 0.2 since c3 above?

u9 = tf.keras.layers.concatenate([u9, c1], axis=3) why you used axis=3 here?

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

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 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

From 3 Channels(R,G,B) how can we make 16 feauture maps? as 16 is not divisible by 3

How I can get the exact code of this tutorial from the github folder?Can anyone say this?

Whats the logic for doing transpose convs instead of upsampling layers?

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.

x='Thank you' while DigitalSreeni > university_professors: print(x)

Hellow, how make tow GPU NVidia geforce 1080 rtx work together in same time for traning ?

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'?

Thanks

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

Why the dropouts are different in different Layer?

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?

Thank you so much

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}

thank you for you're videos very informative. what version of tensorflow you are using in this tutorial, thankyou

Thanks!

can you give me the link of video that is for setting the GPU

Hello, Thank you for your work and time. how can I applicate 219-unet_model_with_functions_of_blocks.py to mnist dataset

InternalError: cudaGetDevice() failed. Status: CUDA driver version is insufficient for CUDA runtime version what is this problem?

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

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?

its so helpful thanks

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')

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

Danke!

Can we use unet architecture for image to image translation

Does the image resolution matters here? I am getting error about different matching shapes when trying to put my resolution instead of yours.

anyone get any error while import the segmentation library ???

why not use data augmentation rescaling to get the floating pixel value?

Hello sir.....which IDE have you used to execute these codes?

Hi fantastic video explaning sematic segmentation and btw are you a telugu guy

Image masks can be created using which tool?

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

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.

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..

Video 72 and U-NET 3rd part (video 75) are missing

Hi, After segmentation how to do feature extraction and classify those features?

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

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?

How do we apply Batch Normalization, if we want to apply? At what stage, should we apply? And how to use that?

is this program applicable to CPU version too

que chevere :3

How to define if unet multiclass? I dont know how to do it somebody please help me :)

which library is better to implement the network : Keras or PyTorch ?

You are amazing

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: kzfaq.info/get/bejne/b8qEmbio07Kaqo0.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?