Questions tagged [image-segmentation]

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What are the most important evaluation metrics for anomaly segmentation?

When people talk about anomaly segmentation models, they often mention evaluation metrics like F1 score, AP, AUROC, and AUPRO. But which one really matters most when comparing models, and why? I'm ...
Mosh Geb's user avatar
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Tensorflow SegNet architecture

I was unable to find a complete description of the SegNet architecture for image segmentation (specifically, the decoder layers). Therefore, I would like to clarify the correctness of my ...
D .Stark's user avatar
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Losing Information while resizing the image in Segmentation task using U-net

I'm using U-net architecture to build a segmentation task of image. During training I have image of size 256256 image. It works very well on the segmentation of same size 256256 or near to size 256*...
Akshit Dhillon's user avatar
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Arabic characters license recognition

I am trying to detect the Arabic characters in a vehicle license plate. I already detect the license plate with good accuracy. However, I tried OCRs on the license plate and the accuracy was horrible. ...
Ahmed's user avatar
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Is there any standard or heuristic for deciding on the dimensions and filters of a convolution layer for image processing?

I reposted this from StackOverflow since it does not meet StackOverflow's guideline to focus on programming and coding questions. Link to the original question. I want to find ways other than trial ...
Joachim Rives's user avatar
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IoU metric for multi class image segmentation task

My input shape is of (168,18). I create batches of size 256 and create my dataset using timeseries_from_Array_dataset. I am visualizing this 2D snapshot of a multivariate timeseries (batch size- 256, ...
Vjs's user avatar
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Why is there a difference in Training Accuracy Output, when the training dataset is the same but the validation dataset is different?

I am looking at the output of a multi-class image segmentation deep learning model. I used U-Net to implement this. I am confused about why the training accuracies are different for a different ...
user10529827's user avatar
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What are good ways to extend an ML model with a new class without relabeling all previous data?

I have a segmentation model trained using 1,000 images that can predict 4 classes (dog, cat, mouse, elephant). I would now like to extend the model with a 5th class (horse). Horses are present in the ...
nickponline's user avatar
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K means clustering of image with k=1 vs mean of all pixels

I have relatively uniformly colored images and I extracted colors using k-means. k means 1 showed the best results for my modeling purposes, k means 2 not so much, and with k-means 3 there ceased to ...
phil27's user avatar
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Is it possible to calculate mAP metric for semantic segmentation task?

I want to calculate mean average precision metric for my semantic segmentation models. Because I want to compare metrics this models with yolov8 instance segmentation model.
Dmitry  Sokolov's user avatar
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Image segmentations vs image detection

If I need to detect on an image some objects and we are only interested in counting them, between image segmentation and object detection which one would you think would yield best results in terms of ...
Dinu Mihai's user avatar
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CNN segmentation models: class weights specification on IoU metric

I am building a MANet model using pytorch lightning. For getting the model I use the library segmentation models. As my objective is to do binary semantic segmentation, during the test phase I ...
Alessandro Pistola's user avatar
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Can unsupervised pretraining (autoencoders) be used for u-nets?

TLDR: Will a u-net pretrained as an autoencoder be able to learn a latent representation of the data if the encoder weights are frozen (can't game the system and pass forward the unmodified image)? ...
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Using AWS Sagemaker (Ground Truth) for Labeling Jobs + Rekognition. How best to approach my project?

I'm new to ML so bear with me. I want to create an object detection model that can detect anything from flags (e.g. Israel flag) to symbols (e.g. yin-yang sign) to a giving setting (e.g. war). I am ...
Noey's user avatar
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Extracting values from a map image (jpg)

Not sure if this is the right platform but I'm in a bit of a fix here. I'm trying to convert this image(jpg) to a raster wherein, the colormap attached shows the values. I'm aware I need to use openCV ...
Ayushi Sharma's user avatar
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How can you add additional features/attributes while doing instance segmentation?

I want to do an instance segmentation of objects in images. Usually I would stick to something like an Mask R CNN and let it run. However additionally to the image itself and the pre-labeled images, I ...
Philipp's user avatar
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Pretrained computer vision models that accept as input a segmented image and the original image

My data is a set of segmented images with extra details: there is 30 object classes each object is labeled with its state (very old, old-fashion, modern) and each object is also labeled with a second ...
Karim-53's user avatar
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Discrepancy in the measured metrics in my segmentation models

I’ve trained my image segmentation models using SegmentationModelsPytorch. Three annotators marked up objects. All pixels that were voted as an object pixel by two annotators were marked as the pixels ...
sixtytrees's user avatar
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How do I get SAM to perform multi-class classification?

I want to use the Segment Anything Model(SAM) to perform multi-class segmentation on satellite images. When I tried to apply it, it ended up giving single-class outputs. Moreover, upon applying a ...
Ipshita Ahmed Moon's user avatar
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What Deep Learning model to use in this spectroscopy task?

I have a task to be solved. There are energy measurements over the square area 40x40. One measurement consists of values : x, y and the energy. The all area is almost whole covered with data (a few ...
Szymon Roziewski's user avatar
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Why does my video segmentation model predict only one value across the entire output array?

I'm working on a video segmentation model, inspired by UNet and VNet. I'm trying to benchmark the model by overfitting it on a single video (done with a 5e-4 learning rate on 5 epochs of 8 steps, with ...
vs07's user avatar
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Custom Loss Function in Tensorflow for UNet

I am working on a Segmentation task, where I planned to use U-Net for the input_image of shape (224,224,3), the output should be the mask image of shape ...
Vishak Raj's user avatar
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1 answer
46 views

MaskRCNN.train gives 'list index out of range'

I have been trying to use MaskRCNN with a Resnet backbone on the DeepFashion2 Dataset for instance segmentation. The custom configurations are as follows: ...
th2797's user avatar
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Can segmentation algorithms like U_Net segment dents in bullets?

I have a dataset of images of bullets. These bullets are to be classified as OK and Not-OK based on the segmentation of defects on the surface of bullets and any dents in the body of bullets. I am ...
The White Cloud's user avatar
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Why do I get such a low accuracy despite having good metrics?

I'm working on an image segmentation problem. I'm fine-tuning the network, and when displaying the metrics I obtain, I have some doubts. I am going to provide some details about the network: Solver: ...
PicaR's user avatar
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How can I interpret this YOLOV7 segmentation results plot?

I have the following results plot generated by train.py of YOLO-V7 segmentation: The problem is that I am not sure how to interpret this plot. For example, to check for overfitting, I need ...
Tlaloc-ES's user avatar
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Segmentation-models-pytorch returns black image

I tried to use Segmentation-models-pytorch first time by following guideline and using it on my task on kaggle(HuBMAP), here is link for official guide: https://github.com/qubvel/segmentation_models....
XEX's user avatar
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1 answer
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What preprocessing should I do to a multiclass segmentation mask?

I am working on a segmentation problem. My masks are tensors with a shape of (4767, 192, 192, 1) --> (num_img, height, width, number of channels). Each mask contains 13 different pixel values (0, 1,...
PicaR's user avatar
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Meta's SAM model: can extract semantic embedding vector?

I'm interested in finding an embedding vector for each segment found by the Facebook/Meta Segment Anything model (for classification and tracking of segments). Can ...
user48956's user avatar
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Embedding vector of MaskRCNN (Resnet with FPN)

I have a MaskRCNN model for instance segmentation with Resnet 50 - FPN backbone trained in detectron2. And I want to extract the embedding/feature vectors for visualizing input and hopefully detecting ...
Sushil Khadka's user avatar
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131 views

How to do batch inference in pytorch on a local GPU?

I'm working on a version of SAM model which predicts the segmentation map using text prompts provided by the user. As of now, running a single image takes about 5 seconds. I want to run about 100 ...
Kiran George's user avatar
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33 views

Detecting scratches in car surface, may be data drift

I am trying to detect scratches on the car surface using Yolo, I have training images like the one below, and I am getting good mAP (around 0.83) on the validation and test dataset. The training image ...
abdul's user avatar
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1 answer
359 views

Can I use one-hot encoded output for segmentation in Pytorch, with focal and dice losses?

know that for classification using a neural network and CrossEntropy Loss, we need one-hot encoded output, but in PyTorch, the CrossEntropy loss does not accept one-hot encoded targets, and we should ...
Shayan Daneshvar's user avatar
1 vote
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43 views

Train multiple models to predict subsets of classes in data

If you are aiming to predict a number of classes that may be too high for the available data, is there any merit to training multiple models to predict a subset of the classes, each, and combining the ...
Zwerchhau's user avatar
1 vote
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106 views

Why is my segmentation model not returning a heat map?

I have implemented two CNN architectures to perform segmentations on medical images: the classic UNet and a modified version called the Attention UNet. I have been training the models on roughly 50,...
Noam's user avatar
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35 views

Obscured object segmentation in binary segmentation

I am using deep learning for building outline segmentation. I have used binary segmentation method using UNet model which runs on Tensorflow 2.0. The output is fine when there are no other object (Ex. ...
Sarath SRK's user avatar
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204 views

Tensorflow Text Segmentation or Open CV Text Segmenation

I want to extract text data from the image, however not sure what approach I should take. Steps successfully performed Dataset collection Image Preprocessing done Image Augmentation done Final Image ...
ZKS's user avatar
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1 vote
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Choosing the right Deep Learning Model for Image Segmentation

How do you choose the appropriate Deep Learning Algorithm if you wanted to do image segmentation with an image datset which consist of hispathology images of almost 10000? I'am new to deep learning ...
user17640477's user avatar
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1 answer
20 views

Mark up text as instance segmentation or semantic segmentation?

I have data with a page of handwriting and I want to segment each word individually. If I mark each word for semantic segmentation, then the model will impose a segmentation mask on each word ...
indian gamedeveloper's user avatar
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43 views

Is my image too low res for semantic segmentation task?

I am trying to solve a semantic segmentation task in the field of agriculture and I have some ortophoto drone images that have been acquired at the same height above the crops and in different ...
Vlad Timu's user avatar
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1 answer
909 views

Which deep learning model is best in terms of instance segmentation and Object detection both?

I am trying to find most efficient and robust Object detector+Segmentation model. I came to know about Mask-rcnn, Yolov5, Yolact, yolov7. As, YOlov7 is new and i read somewhere that yolov7 surpasses ...
Hamza's user avatar
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2 votes
0 answers
923 views

Applyingv a 2D mask onto a 3D rgb color list

Problem I have the following image data as a 3D numpy array containing rgb values of the image in a (n,n,3) shaped list (Image). I also have data of the corresponding black and white mask image in a (...
Hector Edu Nseng's user avatar
1 vote
1 answer
147 views

Certain Image Augmentation Prevent Unet Model from Learning

I am training a Unet model for cell image segmentation from microscopy images. In order to help the model generalize better to different microscopes, I attempted to apply brightness augmentation to ...
Berk Yalcinkaya's user avatar
1 vote
2 answers
187 views

Can you use a trained image segmentation model to label more training data for itself?

Labeling images for semantic segmentation can be expensive. Is it viable to train a model (such as Unet) to a good accuracy and then use this model to label more images to be used as further training ...
Berk Yalcinkaya's user avatar
2 votes
1 answer
63 views

Is batch size of 1 a valid choice for a very deep neural network with high memory requirement?

I am training a very deep neural network (Panoptic-DeepLab) with a ResNet34 backbone on Google Colab on CityScapes dataset for Panoptic Segmentation, and noticed that, with a big crop size, the batch ...
A_C's user avatar
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2 votes
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304 views

Are more target labels in a multi-label classification always better?

Context We work on medical image segmentation. There are a lot of potential labels for one and the same region we segment. There can be different medically defined labels like anatomical regions, more ...
Spenhouet's user avatar
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1 vote
2 answers
731 views

how to label 3d model for segmentation task

I'm working on 3d meshes dataset, i have to label it to train my deep learning model for a segmentation task like the picture shows. I spent days looking for a tool to label my 3d data but ...
hamza mon's user avatar
1 vote
0 answers
70 views

Post processing in medical segmentation with attemtion unet

I am doing a lesion segmentation for multiple sclerosis (MS), and at the moment I am using a attention unet for my thesis. The best validation dice score I have recieved is 0.771 and train 0.84. I am ...
aram's user avatar
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77 views

Is it possible (if yes, then how) to provide the semantic segmentation results on the Original Size of the input image?

Just like this website, it does not matter what shape of image you provide them, instead of giving you a fixed size output, they'll segment your image and provide you the resulting image with original ...
Deshwal's user avatar
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1 answer
346 views

How to measure the similarity between two medical images of different imaging modalities according to similar objects in both of them?

I have two series of medical images each one from different imaging modalities. According to that, I have been segmented the Region of interest (the object which appears in both modalities )using U-...
Fadil's user avatar
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