Questions tagged [u-net]
For questions related to the U-net, a neural network proposed in "U-Net: Convolutional Networks for Biomedical Image Segmentation" (2015) by Olaf Ronneberger et al. for semantic segmentation.
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Image segmentation with varying resolution
I am looking to create a model that is able to perform binary segmentation of images with varying resolutions. For model should be able to classify tree or not tree regardless of the resolution of the ...
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What is the best type of input for a 3D UNet?
I want to use 3D U-Net (or similar) network to create a 3D reconstruction of my microscopy data.
The original paper for the 3D U-Net (https://arxiv.org/abs/1606.06650) describes the implementation ...
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Which models can be applied recursively?
I come from a math background, so I am not up-to-date with machine learning literature.
For the purpose of learning dynamics, I would like to train a model to minimize the following loss:
$$\mathcal{L}...
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Wondering why UNet architecture doesn't predict well. I have more information within the body of the question
I have 45K images of training set (3, 256, 256) same size and it's corresponding output is a 3D tensor with 26 masks (26, 256, 256). I have been training many times without understanding why it doesn'...
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What is the role of skip connections in U-Net?
I was able to find that the skip connections used in U-Net help to recover fine grained details in the prediction, however I do not understand what is meant by this. Besides, I was wondering what ...
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Why diffusion model always use U-Net?
I want to know why diffusion models always use U-Net.
In my opinion, they use U-Net because you can see features of different resolutions and skip connection is good to add detail of images. But I am ...
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Learning an identity function with convolutional networks
I am trying to train networks to achieve what I expected to be a trivial task: learn the identity mapping. However, this is very hard to achieve, and the optimization is hard.
Moreover, I don't want ...
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U-Net Maxpooling vs Convolution
Hello I'm implementing a CycleGAN and most of the other implementations I've seen on the internet use Convolution with stride 2 instead of a Maxpoolinglayer for downsample.
On to my question, why ...
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What are the benefits of using multiple convolutions, as opposed to one, before the pooling layer in a U-Net?
I have seen U-Nets that use a single convolution before the pooling operator and some that use two or more.
My question is, what is better? Or what are the benefits of using more or less convolutions?
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Use of Mask in U Net for plant disease detection [closed]
I am using U-Net for plant disease detection. I am new to deep learning and computer vision.
Currently, we are feeding the masking images generated via open cv HSV format to detect colours from the ...
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Training a u-net for multi-landmark heatmap regression producing the same heatmap for each channel
I’m training a U-Net (model below) to predict 4 heatmaps (gaussian centered around a keypoint, one in each channel). Each channel is for some reason outputting the same result, an example is given of ...
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How does the skip connection match its dimension to the same layer in the expansive path?
According to the U-Net architecture image from the second page of the research paper (URL link) https://arxiv.org/pdf/1505.04597.pdf
How does the skip connection match its dimension to the same layer ...
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Why is the simplest U-Net architecture giving the best (but not good enough) results on a multi-class segmentation on microscopic data?
Currently, I'm trying to optimize a training process of a neural net to improve final results. The problem I'm dealing with is multiclass segmentation on microscopic data.
The paradox is that the best ...
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Pixel values of segmap in multi-class semantic segmentation
I'm preparing a dataset for a multiclass semantic segmentation using U-Net like architecture. To be precise, I've got it ready but a question came to my mind. How does pixel values of a segmentation ...
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Unet Overfitting for binary segmentation of fake images
I am working on a project where I am trying to detect and localize forgeries in images. I am using the CASIA v2 dataset and using Unet model for the task. I have the binary masks of all the images in ...
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What does the "number of channels" correspond to in U-Net?
I'm studying the U-Net CNN architecture. I'm new to CNNs and am confused regarding the "number of channels".
Referring to the U-Net diagram, the input image is convolved with a 3x3 mask ...
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How should I incorporate numerical and categorical data as part of the inputs to the U-net for semantic segmentation?
I am using a U-Net to segment cancer cells in images of patients' arms. I would like to add patient data to it in order to see if it is possible to enhance the segmentation (patient data comes in the ...
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Why doesn't U-Net work with images different from the dataset?
I have implemented a U-Net, similar to this implementation, but for a different dataset, this one, to segment roads.
It works fine using the test folder images, but, for example, when I pick a print ...
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How to use mixed data for image segmentation?
I have a task for which I have to do image segmentation (cancer detection on MRIs). If possible, I would also like to include clinical data (i.e. numeric/categorical data which comes in the form of a ...
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Late Onset Augmentation
If I train a U-Net model for image segmentation (e.g. medical images) and start training until it converges and then add augmentation - can i expect similar results as if i train with augmentation ...
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Why does my model not improve when training with mini-batch gradient descent, while it does with Adam?
I am currently experimenting with the U-Net. I am doing semantic segmentation on the 2018 Data Science Bowl dataset from Kaggle without any data augmentation.
In my experiments, I am trying different ...
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How and why do state-of-the-art models in medical segmentation differ from general segmentation models?
I am just getting into medical image segmentation and have been able to understand the state-of-the-art architectures, like Double UNet, UNet++, and Multiresunet.
What I haven't understood yet: Why ...
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Have I understood the loss function from the original U-Net paper correctly?
In the original U-Net paper, it is written
The energy function is computed by a pixel-wise soft-max over the final
feature map combined with the cross entropy loss function.
...
$$
E=\sum_{\mathbf{x} ...
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Semantic segmentation CNN outputs all zeroes
I'm using MATLAB 2019, Linux, and UNet (a CNN specifically designed for semantic segmentation). I'm training the network to classify all pixels in an image as either cell or background to get ...
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What is the use of the regular convolutional layer in expansion path of U-Net?
I was going through the paper on U-Net. U-net consists of a contracting path followed by an expanding path. Both the paths use a regular convolutional layer. I understand the use of convolutional ...
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Validation accuracy higher than training accurarcy
I implemented the unet in TensorFlow for the segmentation of MRI images of the thigh. I noticed I always get a higher validation accuracy by a small gap, independently of the initial split. One ...
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Hairstyle Virtual Try On
I want to help people with cancer who are under chemotherapy, and generally people who have lost their hair to Virtually Try-On Toupees/Wigs on their head.
VTO must support both the frontal and side ...
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Why do I get higher average dice accuracy for less data
I am working on image segmentation of MRI thigh images with deep learning (Unet). I noticed that I get a higher average dice accuracy over my predicted masks if I have less samples in the test data ...
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Semantic segmentation failing in small instance detection
I performed semantic segmentation with U-net. My dataset consists of grayscale images of defects. After training the dataset for I got an metric accuracy of only 0.3 - 0.4 IOU. Eventhough it is merely ...
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Could the data augmentation lead to the model learning features which corresponds to data augmented data and not to the real data?
I am trying to train a Unet network with Synthetic data to do binary segmentation due to the fact that is is not easy to collect real data.
And there is something in the training process that I do not ...
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Getting bounding box/boundaries from segmentations in UNet Nuclei Segmentation
From my understanding, in a tissue where nuclei are present and need to be detected, we need to predict bounding boxes (either rectangular/circular or in the shape of the nucleus, i.e. as in instance ...
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Model output segmentation maps which are not full
I created a VGG based U-Net in order to perform image segmentation task on yeast cells images obtained by a microscope.
There are a couple of problems with the data:
There is inhomogeneity in the ...
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What is the time complexity of the upsampling stage of the U-net?
I am trying to determine the complexity of the neural network we use. The neural network is a U-net generator with an input shape of NxN (not an image but image-like data) and output of the same shape....
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How to quickly change hand-drawn shapes to symmetrical polished shapes?
Given a hand-drawn shape, I'd like to generate the corresponding symmetrical polished shapes such as circle, rectangle, triangle, trapezoid, square, parallelogram, etc.
A short video demonstration
...
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Is it necessary to label the background when generating the labelled dataset for semantic segmentation?
When I label images for semantic segmentation (using u-net, if that matters), is labeling the background (anything I am not interested in) necessary? Will it improve the network's performance?
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Suppress heatmap non-maxima in segmentation with UNet
I'm using U-Net for image segmentation.
The model was trained with images that could contain up to 4 different classes. The train classes are never overlapping.
The output of the UNet is a heatmap (...
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What are some good alternatives to U-Net for biomedical image segmentation?
Soon I will be working on biomedical image segmentation (microscopy images). There will be a small amount of data (a few dozens at best).
Is there a neural network, that can compete with U-Net, in ...
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Why everyone is using CNN for image segmentation?
I'm a newbie in artificial intelligence.
I have started to research how to do image segmentation and all the papers that I have found are about CNN. Most of them use the same network, U-net, but with ...
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If I trained a model to perform semantic segmentation on images with only one object, would it also work on images with multiple objects?
I'm working on semantic segmentation tasks in the medical space using the U-Net. Let's say that I train a U-Net model on medical images with the goal of segmenting out, say, ligaments, from a medical ...
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How to train image segmentation task with only one class?
Is there a neural network that has architecture optimizations for segmenting only one class (object and background)? I have tried U-net but it is not providing good enough results.
I am wondering if ...
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Using U-NET for image semantic segmentation
I'm getting literally crazy trying to understand how U-NET works. Maybe it is very easy, but I'm stuck (and I have a terrible headache). So, I need your help.
I'm going to segment MRI to find white ...
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Would models like U-Net be able to segment objects which has label based on its surrounding context?
Suppose that we want to segment a red blob from the image, normally you will have a class for this red blob e.g. 0. And every red blob you detected will have a class of 0.
But, in my case, I want ...
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What are the best algorithms for image segmentation tasks?
I recently started looking for networks that focus on image segmentation tasks related to biomedical applications. I could not miss the publication U-Net: Convolutional Networks for Biomedical Image ...
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What are some neural network models that can use auxiliary info during training for image segmentation?
What are some deep learning models that can use supplementary information other than RGB channels for image segmentation?
For example, imagine a poorly shot image of a river (blue) that shows a gap, ...
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How can I use the bottleneck layer of the U-net to calculate the similarity between two images?
I would like to use the bottleneck layer of U-Net (the last layer of the encoder) to calculate the similarity between two images. For that, I have to somehow flatten the last layer of the encoder. In ...
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How do we stack two U-Nets to yield one final prediction?
I am trying to reproduce the model described in the paper DocUNet: Document Image Unwarping via A Stacked U-Net, i.e. stacking two U-Nets to yield one final prediction. The paper mentions that:
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