208 - Multiclass semantic segmentation using U-Net

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Code generated in the video can be downloaded from here:

The dataset used in this video can be downloaded from the link below. This dataset can be used to train and test machine learning algorithms designed for multiclass semantic segmentation. Please read the Readme document for more information.

To annotate images and generate labels, you can use APEER (for free):

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This channel deserves millions of subscribers. Thanks for the amazing contents.

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Needed this so much. Seems like every time I run into a problem with my research you put out a video answering my prayers. Thanks Sreeni.

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One of the best channels for Research Students of Computer Vision discipline.

awaisahmad

Exactly what I was looking for, you are a very knowledgeable person with a great talent for explaining things!!! Please don't stop!

junkmail

Thank you sreeni for the labelencoder path, all other places it was simply -1, but my masks were in color and i just realised that differnce after wathing this super helpful insight.

suyashdahale

Thanks for Multiclass segmentation. In Segmentation or even Image related Deep Learning your Videos are

jknitk

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kannanv

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vaveileinn

Wow the best explain of these concepts I have seen in a long time. Thanks for this

jacobusstrydom

thanks I was just working on a multiclass segmentation with Unet

applejuice

Your U-net videos are very helpful for me.

I would appreciate if you could produce videos on instance segmentation as well and particularly Mask RCNN model. Thanks a lot. 🙏🙏

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Best YouTube channel for deep learning researchers.

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Sreeni thank you so much for all the work you put into these videos. It has helped me so much get started with segmentation

EUMikkel

Thank you. Your tutorials are life savers for me

kavithashagadevan

Thank you for your tutorial. I would like to request an open-slide tutorial for generating patches from the whole-slide images. This is very important for the analysis of histopathology images.

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Thank you, your tutorials are one of the best.

bikkikumarsha

Finally video explained to details. Thanks

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Thank you for your tutorials and lectures.

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thanks for the contribution, appreciated.

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Thank you!

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