Rembg – background segmentation tool using machine learning

Last Updated on March 6, 2023

Human segmentation model

By default the software uses the U2Net model. But it’s also possible to use alternative models with the -m or --model flag. There are four models available: U2Net, U2NetP, U2Net Cloth Segmentation, and U2Net Human Segmentation.

For example:

$ rembg i -m u2net_human_seg human-input.png human-output.png

U2Net and U2NetP have the same network architecture but differ in the number of input and output FeatureMaps. Essentially U2NetP is a lightweight version. The Cloth Segmentation offers a pre-trained model for cloths parsing from human portrait.

We want to remove the background from this image.


Using the default U2Net model, we issue the command:

$ rembg i people-3104635_960_720.jpg people-u2net.jpg

As you can see the results with U2Net are far from perfect.


Let’s rerun Rembg but this time use the model for human segmentation (u2net_human_seg).

$ rembg i -m u2net_human_seg people-3104635_960_720.jpg people-u2net-human-seg.jpg


Next page: Page 4 – Summary

Pages in this article:
Page 1 – Introduction / Getting Started
Page 2 – In Operation
Page 3 – Human segmentation model
Page 4 – Summary

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