Stack Overflow
Stack Overflow

Questions tagged [semantic-segmentation]

Semantic segmentation is the task of classifying every pixel in the image to a category or class label.

Semantic segmentation is the task of classifying every pixel in the image to a category. It refers to the process of linking each pixel in an image to a class label. Some of its primary applications are in autonomous vehicles, human-computer interaction & robotics.

532 questions
2
votes
2 answers

Can't fit data to 3d convolutional U-net Keras

I have a problem. I want to make 3D convolutional U-net. For this purpose I'm using Keras. My data are MRI images from Data Science Bowl 2017 Competition. All MRI's were saved in numpy arrays (all pixels are scaled from 0 to 1) with…
Max Titkov
  • 185
  • 1
  • 11
1
vote
1 answer

how to produce image with semanticSegmentationSkyMatteImage information on iOS?

I'm trying to create a sky mask on pictures taken from my iPhone. I've seen in the documentation that CoreImage support semantic segmentation for Sky among other type for person (skin, hair etc...) For now, I didn't found the proper workflow to use…
Damien Jardon
  • 11
  • 1
1
vote
0 answers

When CNN padding is disabled, network doesn't learn

I’m working on an unsupervised segmentation pipeline which involves first learning a SLIC segmentation with the CNN. This works as expected when all layers in the network use padding, however, for reasons specific to this pipeline, I would like to…
Isaac Wasserman
  • 83
  • 7
1
vote
1 answer

How to merge image of many channels?

I'm trying use multi-channel file to training unet classify. My dataset is 5 * 1-channel & 1 * 3-channel image, and I expected it can be 8-channel .npy file. I use np.concatenate to merge, but it cannot apply on a 1-channel image. Here is my…
Syuuuu
  • 1
  • 5
1
vote
1 answer

Incorrect positions of annotation polygons when drawing with Detectron2 Visualizer

Hello Stack Overflow community, I apologize if my question seems trivial. I am currently working on building detection from aerial PNG images. Each image has dimensions of 2000 pixels by 2000 pixels with a resolution of 20cm. To accomplish this, I…
Alfa
  • 23
  • 6
1
vote
2 answers

Grouping Similar Annotation Semantically

I have a TSV file that is the output of the Semantic-Segment-Anything model. In the model, the detection of objects may have different annotations. For instance, I am detecting my objects based on their X and Y coordinates. In my table, there is a…
Shalqam
  • 11
  • 2
1
vote
1 answer

Runlength encoded mask is not as same as decoded mask

I'm dealing with a task of doing semantic segmentation on Severstal steel anomaly detection, These are the encoding and decoding functions. def mask_to_rle(mask): """ params: mask - numpy array returns: run-length encoding string (pairs…
SANJAY. R
  • 65
  • 5
1
vote
0 answers

What implications do reduce_zero_label and ignore_label have in mmseg database configuration?

I am currently training a semantic segmentation model in a database with 3 classes: background, classA and classB. Background class is represented as [0,0,0] (value 0 in annotation image), classA is [0,255,0] (value 1 in annotation image) and classB…
Raul l
  • 11
  • 1
1
vote
1 answer

Detectron2 saving output file as json

I would like to save the output image as json for future annotation. The following code is able to show the output prediction. Would like to seek your help to assist me on how to save the output as json format. Many thanks. from…
Lim Cheng Wei
  • 11
  • 3
1
vote
0 answers

when using Dice loss, should the predections be probabilites like [0.3,0.7] or I should set threshold to make them either 0 or 1 like [0,1] (trsh=0.5)

I'm using Unet model and diceloss for binary segmentation with 1 channel. I'm really confused if I should give the dice loss the predections as they are (after applying sigmoid) or I have to set threshold before passing it to the loss function I…
abdelmadjid tahar
  • 23
  • 2
1
vote
0 answers

Mean Dice loss on train negative values

I am training an Unet arch on Medical image semantic segmentation, supervised learning - images/masks and I am getting weird numbers for DICE loss Vanila_UNet Epoch [0] Mean loss on train: -140.31943819224836 Mean DICE on train: 1.7142934219089918…
jokkk2312
  • 61
  • 5
1
vote
0 answers

How do I segment head and body in a top-down depth map of a cow?

I am using OpenCV to process images and extract the largest area from an area and remove any background information. The images are pictures of cows, used to estimate their weight. To estimate the weight correctly, I need to remove the head from the…
Katie Price
  • 31
  • 1
1
vote
1 answer

Calling invoke_endpoint from AWS Lambda for a semantic segmentation model returning application/x-recordio-protobuf instead of image (Python)

I'm calling a semantic segmentation model from AWS Lamba with the invoke_endpoint function but instead of getting an image response I get a application/x-recordio-protobuf which I'm unable to convert to an image for further processing. The semantic…
user3466555
  • 25
  • 3
1
vote
1 answer

Feature matching between segmented images

I would like to improve the feature matching between two images by using a pre-trained semantic segmentation model. Of course I am familiar with the ORB or SIFT features and matching with OpenCV in python, but I am curious if it is possible to…
Kuba Głowacz
  • 49
  • 1
  • 6
1
vote
1 answer

How to train a semantic segmentation model with multiple masks?

I am working on a project about semantic segmentation in medical concepts where each image has multiple masks and each mask shows a different disease in the human eye, now I don't exactly know how to train my model with multiple masks. Should I…
fatemeh_p
  • 21
  • 4
1 2 3
…
35 36