U²-Net Cloth Segmentation (u2net_cloth_seg) clothing segmentation#

U²-Net trained to parse clothing on people into upper body, lower body and full body masks.

Run it: rembg i -m u2net_cloth_seg input.jpg output.png setup

How to run u2net_cloth_seg

u2net_cloth_seg ships with rembg as the session u2net_cloth_seg. The weights download on first use.

Command line

pip install "rembg[cpu,cli]"
rembg i -m u2net_cloth_seg input.jpg output.png

Python

from rembg import new_session, remove
from PIL import Image

session = new_session("u2net_cloth_seg")  # load once, reuse for every image
output = remove(Image.open("input.jpg"), session=session)
output.save("output.png")

Other sessions: all rembg models.

Quick take

Best for: Cutting garments off a model photo, e.g. for fashion catalogs
Avoid when: You want the whole subject cut out: use u2net_human_seg or birefnet-portrait
Notes: Not a background remover, so it is not on Bench v0. rembg returns the cutouts stacked vertically; pass `cc` (`-x '{"cc": "upper"}'`) to pick one class.

When a hosted model is worth it

u2net_cloth_seg is free and runs on your machine; keep it if the results above are good enough. If you need finer hair and edges, or don't want to run large models locally, rembg also ships the withoutBG Pro Model as the withoutbg session, ranked #1 of 17 by accuracy on Bench v0. Images are sent to withoutBG's servers.

Same command, hosted model

export WITHOUTBG_API_KEY=sk_...
rembg i -m withoutbg input.jpg output.png