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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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UNets with a pretrained network as the encoder portion of U-Net

UNets with a pretrained network (like VGG16 or InceptionV3 or ResNet, or …) as the encoder portion of U-Net are common. However I'm struggling to understand how the 1D encoded second-to-last layer is ...
FluidMechanics Potential Flows's user avatar
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35 views

Advice on Moving Object Segmentation with U-Net where the target is small

I have a problem I'm trying to solve. I'd like to spot a moving object in a video sequence. "Moving Object" is very vague, but it can be roughly defined as 'here is a bright point that seems ...
Oni's user avatar
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17 views

Why does UNet often output noisy pattern in blank/homogeneous region?

I am recently implementing DDPM model from scratch, and I discovered that UNet often tends to give noisy output in blank region. Here is an example with FashionMNIST, my DDPM seems to generate OK ...
Dibbla's user avatar
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1 answer
43 views

How do I input multi-channel Numpy array to U-net for semantic segmentation

I had lidar 3D point cloud data from semantckitti. I want to perform Semantic Segmentation on the data using U-Net. I converted the 3d point cloud data into 2D using spherical conversion and saved the ...
Leibniz 24's user avatar
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31 views

How does diffusion models "imagine" a new image from prompt?

When a new image is generated through diffusion denoising, for instance: I wonder if the resulting "Avocado Armchair" is purely using only those 2 images above it, or is drawing learned ...
James's user avatar
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1 answer
50 views

How to remove random noise from an image (denoising)?

When adding noise to an image, for instance, is the noise added evenly random (equally likely values within some range), or random but following some distribution (like the normal distribution)? Then,...
James's user avatar
  • 157
3 votes
1 answer
182 views

How to perform latent space Interpolation between two images?

I have a variational convolutional autoencoder that has trained on 2 images and outputs a linear interpolation (inserted at the bottleneck stage) between those 2 input images. However, the result ...
James's user avatar
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14 views

How to apply Latent Diffusion for 3D Binary Voxel Data?

Suppose we have a voxel of shape (60, 36, 60) with values 0 or 1 (1-occupied, 0-empty). What is the possible architecture of latent diffusion?
Renat Abdrakhmanov's user avatar
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1 answer
38 views

3D Unet gives "output size is too small" error [closed]

I wrote simple 3D-Unet arch in pytorch to do segmentation on 3D images. ...
user1631306's user avatar
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0 answers
33 views

Where U-Net and Convolutional layers are settled in Stable Diffusion model?

When I read about Stable Diffusion model, they usually talk about adjusting convolution layers or U-Net weights. I believe they both should be related together and the U-Net is the part that accepts ...
best_of_man's user avatar
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29 views

Deployment of Model into Java Application

Over the past few months, I have developed and tested a TensorFlow U-Net model based on the Keras Functional API. Recently I have been interested in deploying this model for use in a Java application ...
Max's user avatar
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Is number of output filters right way to generate multiple output feature maps?

I am doing a CNN U-Net localization network that works on a way of scalar regression of 2D plane using Gaussian peaks in Keras. I have dataset that contains 10 different keypoint PNG's (each about 50k ...
wortelus's user avatar
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1 answer
141 views

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 ...
cmosig's user avatar
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0 answers
61 views

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 ...
sam's user avatar
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1 answer
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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}...
user572780's user avatar
2 votes
1 answer
7k views

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 ...
TRM's user avatar
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4 votes
3 answers
9k views

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 ...
Penguin.jpg's user avatar
3 votes
1 answer
236 views

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 ...
Franco Marchesoni's user avatar
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1 answer
153 views

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 ...
Zitrus's user avatar
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0 answers
53 views

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?
Lukas Pezzei's user avatar
-1 votes
1 answer
181 views

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 ...
emcsquare's user avatar
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494 views

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 ...
Solace's user avatar
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0 votes
1 answer
505 views

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 ...
wooong's user avatar
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0 votes
0 answers
151 views

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 ...
Nuwanda's user avatar
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1 vote
0 answers
68 views

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 ...
Nuwanda's user avatar
  • 11
4 votes
2 answers
4k views

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 ...
Yishu Malhotra's user avatar
2 votes
1 answer
6k views

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 ...
Izzo's user avatar
  • 123
0 votes
1 answer
393 views

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 ...
Skyris's user avatar
  • 115
0 votes
0 answers
25 views

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 ...
FourZeroFive's user avatar
1 vote
1 answer
160 views

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 ...
Skyris's user avatar
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1 vote
2 answers
79 views

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 ...
Samuel Peterson's user avatar
1 vote
1 answer
138 views

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 ...
Bert Gayus's user avatar
2 votes
0 answers
71 views

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 ...
Bert Gayus's user avatar
1 vote
1 answer
680 views

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} ...
Bert Gayus's user avatar
-1 votes
2 answers
288 views

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 ...
The Impossible Squish's user avatar
3 votes
1 answer
225 views

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 ...
Bhuwan Bhatt's user avatar
8 votes
1 answer
641 views

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 ...
Lis Louise's user avatar
1 vote
0 answers
1k views

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 ...
aliep's user avatar
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2 votes
0 answers
88 views

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 ...
Lis Louise's user avatar
0 votes
1 answer
188 views

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 ...
shankar ram's user avatar
1 vote
0 answers
34 views

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 ...
Thomas R's user avatar
  • 111
2 votes
0 answers
591 views

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 ...
Prasanjit Rath's user avatar
1 vote
0 answers
22 views

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 ...
David's user avatar
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1 vote
1 answer
633 views

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....
Ruli's user avatar
  • 153
1 vote
1 answer
108 views

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 ...
JammingThebBits's user avatar
1 vote
0 answers
433 views

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?
liorr's user avatar
  • 111
1 vote
0 answers
209 views

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 (...
luke88's user avatar
  • 111
4 votes
1 answer
6k views

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 ...
Nuwanda's user avatar
  • 41
2 votes
1 answer
201 views

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 ...
VansFannel's user avatar
3 votes
3 answers
877 views

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 ...
cmed123's user avatar
  • 131