Bild
Interactive-Image-Background-Remover
Point-and-click AI background removal with Segment Anything and BiRefNet
★ 59 SterneCustom
Anleitung ansehen →SAM 2 ist ein Open-Source-Projekt aus der Kategorie Bild von facebookresearch (facebookresearch/sam2 auf GitHub). Es hat 20.061 Sterne auf GitHub und 2.581 Forks, wird unter der Lizenz Apache-2.0 veröffentlicht und wurde zuletzt am 30. Mai 2026 auf GitHub aktualisiert.
SAM 2 — Meta's Segment Anything Model 2: promptable segmentation for images and video, Apache-2.0
SAM 2 needs Python 3.10+, torch 2.5.1+ and torchvision 0.20.1+ — install those from the PyTorch site before anything else.
Clone the repo and install it in editable mode; add the notebooks extra for the example notebooks.
Download a SAM 2 checkpoint, then run image or video prediction with the predictor from the example notebooks.
Point, box or mask prompts select exactly the object you mean — no training on your subject required.
The step beyond SAM 1: the same model tracks a segmented object across video frames.
Image prediction and video prediction notebooks show the full workflow, with jupyter and matplotlib as the only extras.
Segmentation masks feed background removal, object cut-out, inpainting and video effects pipelines.
The README install route: clone, then editable install.
git clone https://github.com/facebookresearch/sam2.git && cd sam2
pip install -e .Required for the example notebooks (jupyter and matplotlib).
pip install -e ".[notebooks]"Checkpoints are separate from the code — download them per the README Getting Started section, then run image or video prediction.
https://github.com/facebookresearch/sam2#getting-startedMeta's Facebook Research team, as the successor to the Segment Anything Model.
Yes — Apache-2.0 licensed code; checkpoints carry their own licence terms.
SAM 2 adds video: it segments and tracks objects across frames, where SAM 1 handled single images.
For practical use, yes — the install compiles a custom CUDA kernel, and the README recommends a GPU machine (WSL on Windows).
Install PyTorch 2.5.1+ and TorchVision, clone and pip install -e ., download a checkpoint, and follow the image or video prediction notebooks.
Point-and-click AI background removal with Segment Anything and BiRefNet
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Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.