Questions tagged [semantic-segmentation]

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Learning Rate Finder doesn't work with Tversky Loss, any idea?

I'm working with a UNet on a binary segmentation problem. As my dataset is extremely imbalanced (sometimes the objects I'm trying to segment are really, really small) I'd like to use the Tversky Loss ...
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How to match the input image with the ground truth image (the order)

I'm a beginner learning deep learning and trying to do semantic segmentation problems on histologic image using python and TensorFlow. There is 2 main file : Images.npy and Labels.npy (Ground truth). ...
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semantic segmentation using kmeans or mean shift

i know what semantic segmentation is and i know how to do semantic segmentation using deep learning but my question here can i do semantic segmentation with a traditional way like kmeans or mean shift ...
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At the first epochs, what will segmentation model get?

I am working at a semantic segmentation problem now, with 5-classes task. But when I running on validation function and output my probablities map. I found that with the background class (the extra ...
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Tensorflow 2 Semantic Segmentation - loss function for two classes

I am trying to implement a U-net for semantic segmentation with two classes (foreground=1 and background=0) in the segmentation mask images, following this tutorial. They have used ...
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How to create the categorical mask for images specifically for Tensor? Or port the NumPy function correctly to Dataset.map function

I'm trying to move from NumPy array as my dataset to tensorflow.Dataset. Now, I've created a pipeline to train the model for classification problems. At some point, I just normalize all the images ...
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Identify visible stones in the image - Approach in OpenCV & Deeplearning

I have samples images of stones present in the images. I need to identify the visible stones only. The approach which I tried is threshold based filtering and detecting cv2.contours. Also, I am ...
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U-Net for Crack Segmentation

I used a U-Net model that was built for Oxford Pet Segmentation to a crack segmentation project. Without transfer learning, model works fine for pet segmentation but not for crack segmentation. What ...
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Do I have to set same batch size for training, validation and testing?

I was performing segmentation task and have set my batchsize to 16 for all train, validation and inferencing. In my observation, I got better result in inferencing when setting batch size to 1. How ...
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one hot encode or not for segmentation when using dice loss

I am trying to perform binary semantic segmentation and using Dice loss as my loss function. I used to perform one-hot encoding in most of my segmentation tasks, especially when using cross-entropy ...
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how to visualize segmented labels in a already existing graph?

I am working on a project where I have to segment the image using multi-class segmentation (3 classes) on microscopic images. Now let's say that I am segmenting solid, liquid and gas images (this is ...
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Understanding last convolution of U-NET for image segmentation

I was trying to understand the last layer of Image segmentation architecture (U-NET). For example what will be the logits-probability distribution of pixels in each case? I know that its there as ...
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Image segmentation with U-net

I am trying to understand if Semantic segmentation with U-NET. Are we training kernels to extract features or are we training a fully connected layer at the end? Or both? If so, how are we training ...
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Remove frame from background

I am having 400 images that look like the following: I would like to remove the frame and only get the image in the middle: I tried the MODNet model ...
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How do you train a semantic segmentation model to optimize for IoU rather than accuracy?

I am currently building a U-NET semantic segmentation model on Tensorflow Keras to classify pixels as belonging to or not belonging to a class. For this problem, I've isolated the masks for only one ...
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Instance segmentation - Which is the best approach for my use case?

I am looking to train an instance segmentation model that will be running on the edge, like on mobile devices. What is the best network (Mask RCNN, DeepLab v2/3 etc.) for this use case? Which gives ...
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Why is my training loss not changing?

I'm trying to train a semantic segmentation model based on this architecture, using this one as a base. The base model uses about 10 ReLU activations, and when implemented according to the first paper,...
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What is the best Panoptic Segmentation algorithm up to date?

A Cityscapes ranking would suggest that EfficientPS and Panoptic-Deeplab are the best, while the Coco ranking suggests other algorithms, more recent, like mask2former. Which one is the best?
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Is there an official procedure to compute mIoU?

Although it sounds silly, I'm not finding an official source to compute mIoU. I'm realizing a semantic segmentation task, and I want to compute the mIoU over a dataset. My doubt is, should I compute ...
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Increasing training accuracy of U-Net segmentation model

I was working with segmentation using u-net and MobileNet. While I trained with input size 256*256 it had an output with Val loss: 0.044 (In this time dense layer was 256, 128, 64, 32 with a learning ...
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resnet50 implementation for semantic segmentation

I am new to resnet models. I want to implement a resnet50 model for semantic segmentation I am following the code from this video, but my numclasses is 21. I have a few questions: If i pass in any ...
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Browser history segmentation

I'm trying to segment a browser history into semantically coherent sessions. For example, a user might be working on a school project for 30 minutes, then planning an upcoming vacation for 30 minutes, ...
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Why my encoder-decoder based segmentation model is producing high loss, when UNet/SegNet is working fine

I have got a UNet and SegNet model implemented on a dataset, with high mean-IoU and accuracy. Now I tried to implement SegNet(not FCN, just encoder-decoder portion) on the same dataset. But for this ...
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The channel dimension of the inputs should be defined. Found `None`

Hello I'm trying to use SegNet in my project with tensorflow, for educational purpose. And I'm surely following someone else's code on GitHub: ...
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Can i segment/crop out image using image processing?

I have a large dataset of bottles. I want to train a model with this dataset. But before feeding the input images to the model I want to crop out the bottle from the background. Is there a way to do ...
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Pre-trained models for semantic segmentation of satellite images

I'd like to use semantic segmentation of satellite images but I don't have the processing power to train existing models or producing one my own. Ideally, I'm looking for a U-Net CNN model that can be ...
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Model comparison: how to explain worse (lower) dice scores but better (lower) Hausdorff distances

I have two segmentation models (U-Net-like architectures): an original model, and an experimental model. I use the dice score and 95% Hausdorff distance to evaluate their performance. Using the first ...
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My semantic segmentation model classifies everything as background

So, I am working on a semantic segmentation task using U-Net. The dataset is very unbalanced, with the background being by far the most common, and the last class being very scarce. First I trained it ...
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Suitable instance counting CNN for training on polygonal masks

I have a medical dataset labeled with polygonal masks (rather than rectangle boxes). It works well for pixel annotation with UNet to generate masks of healthy vs damaged skin. Now I need to do ...
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High image segmentation metrics after training but poor results in prediction

I'm trying to build a model with Keras that predicts four classes of features from microscopy noisy images which cover about 10 - 30 % of the image. I'm using U-net because my dataset is small (150 ...
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Criteria for saving best model during training neural network?

I am doing 4-class semantic segmentation with U-net using generalised dice loss as loss function. General approach to save best model during training is to monitor validation loss at each epoch and ...
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Pixel labelled image is must for semantic segmentation using deep learning in matlab?

I have large set of CT images containing lung regions. But I don't have corrosponding pixel labelled images. So how I could do semantic segmentation using DL in matlab. Like a test program I created 6 ...
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Correct way of computing dice score for image segmentation?

In binary image segmentation, for given a set of images, it's true mask and predicted mask. How do you compute dice score? Should I compute the dice score for each image separately and then find mean ...
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Multiclass semantic segmentation with some classes possibly not present in some of the images

Let's assume we have a large annotated dataset with 4 classes. In this dataset, there might be annotated images with less than 4 classes, where the remaining classes might or might not be present. As ...
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What is the difference in computational cost at inference time between object detection and semantic segmentation?

I am aware that YOLO (v1-5) is a real-time object detection model with moderately good overall prediction performance. I know that UNet and variants are efficient semantic segmentation models that are ...
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Which F1-score is used for the semantic segmentation tasks?

I read some papers about state-of-the-art semantic segmentation models and in all of them, authors use for comparison F1-score metric, but they did not write whether they use the "micro" or &...
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Semantic segmentation of an image with multiple labels per pixel

I am building a model for a multiclass sematic segmentation of a skin disease. At a moment I am using U-Net for binary classifications. In this multiclass problem I have the following cases. There are ...
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Utilizing 1x1(x1) convolutions as a learned max pooling (3D)?

I have a semantic segmentation network that ingests 3D images (hyperspectral $(x, y, b)$) and predicts 2D images (semantic map $(x, y)$). This network takes the form of a classic UNet, though it ...
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Semantic segmentation to instance segmentation

Having a mask from semantic segmentation I want to split it to list of non-overlapping instances masks(like an output from instance segmentation). Do you know some fast algorithm, approach, tool or ...
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What is Deep supervision?

I'm interested in segmentation models for medical imaging purposes. When I looked at the state of the art, I fell on a paper on a new architecture, Unet++: UNet++: A Nested U-Net Architecture for ...
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Tool for annotation of images for semantic segmentation

I have been searching around for a software tool, that I can use for annotating images. More specifically I want to do annotation to be used for semantic segmentation, meaning I want to create masks. ...
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Image segmentation with large class imbalance leads to zero precision/recall

I have a binary semantic image classification problem where only very small parts of the images are positive, most of it is negative. In the training data I have a positive rate of around 0.023, which ...
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2 answers
106 views

How to extract contents by topic from a document?

I am trying to extract information from resumes. I tried the pdfminer for the text extraction. But I need to extract the contents from a resume with respect to its title. For example: I will be giving ...
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Semantic networks: word2vec?

I have some doubts on how to represent the relationships between words in texts. Let’s suppose I have two sentences like these: ...
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For semantic sementation, why am I getting better loss values with binary cross entropy than dice coef?

I'm learning all related to data science and how to train U-Net to do semantic segmentation. I have a U-NET with this loss function: ...
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How to approach coarse-grained semantic segmentation?

I’m looking at doing something like semantic segmentation of images but where I only have pretty coarse-grained labels - roughly, for each 32x32 patch, I know if the answer should be “yes”, “no” or “...
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