In the Semantic Segmentation Using Deep Learning tutorial how can I create my own datasets

Hi,

As the title suggests, I want to replicate the work done here: https://uk.mathworks.com/help/vision/examples/semantic-segmentation-using-deep-learning.html

I am unsure of the best way to make my own dataset to fit this model. I have all of the input images I need, but I don't know the best way to quickly label (color) them in the same fashion.

Is there an efficient way to do this for several hundred files?

[Prerak Mody]

You could refer this blog post on the website of Playment - A Definitive Guide To Build Training Data for Computer Vision

Refer to the section - Choosing Image Annotation Tools

[suman webtunix]

For more detail about Semantic Segmentation and Bounding Box Annotation You can click Here: Webtunix AI.

Answer 1

[Asanka Perera]

1. Use Matlab image labeler and label all your input images.

2. Export the pixel labels using "to file" option. Then, you can see "PixelLabelData" folder with blank .png images. They are not blank and you can use imagesc to view them.

3. Convert all the images in PixelLabelData folder to RGB images using label2rgb command, now you have a dataset similar to this example.

[nacerdine larbi]

plzz can you explain me how can i do a semantic segmentation if you have a example to show me plz

Answer 2

[Aishwary Jagetia]

Manually tagging the data is not more a hurdle. Check this out,

https://www.youtube.com/watch?v=tYqnsp-OcLQ

https://www.youtube.com/watch?v=xkSEnDIlvhI

you can quickly create your own image and video segmentation data in no time!!

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