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built-in AI models such as U2Net. It runs as a CLI, a Python library, an HTTP server, or a Docker container, and inference happens locally so images never need to leave your machine for a third-party service.\u003C\u002Fp>\n\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>Item\u003C\u002Fth>\n\u003Cth>Value\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>Platforms\u003C\u002Ftd>\n\u003Ctd>Linux, macOS, Windows, Docker\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Language\u003C\u002Ftd>\n\u003Ctd>Python 3.11 to 3.13\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>License\u003C\u002Ftd>\n\u003Ctd>MIT\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Inference engine\u003C\u002Ftd>\n\u003Ctd>ONNX Runtime\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Cblockquote>\n\u003Cp>Local AI background removal — one command lifts the subject out of the frame.\u003C\u002Fp>\n\u003C\u002Fblockquote>\n\u003Ch2 id=\"key-features\">Key Features\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>One-click removal\u003C\u002Fstrong>: feed it any image and it detects the subject, returning a transparent-background result\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Multiple modes\u003C\u002Fstrong>: command line, Python library, HTTP server, and Docker container\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Model choice\u003C\u002Fstrong>: ships with u2net, isnet, and other models that can be swapped per scene, people or objects\u003C\u002Fli>\n\u003Cli>\u003Cstrong>CPU and GPU\u003C\u002Fstrong>: CPU inference by default, with onnxruntime-gpu acceleration for NVIDIA cards\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Alpha matting\u003C\u002Fstrong>: optional edge refinement makes fine details like hair blend naturally\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Batch processing\u003C\u002Fstrong>: the CLI can process a whole directory of images\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"target-audience\">Target Audience\u003C\u002Fh2>\u003Cul>\n\u003Cli>E-commerce and asset production staff\u003C\u002Fli>\n\u003Cli>Content teams batch-processing images\u003C\u002Fli>\n\u003Cli>App developers embedding background removal\u003C\u002Fli>\n\u003Cli>Privacy-conscious designers who need local processing\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"quick-start\">Quick Start\u003C\u002Fh2>\u003Col>\n\u003Cli>Install the backend that matches your hardware\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cdiv class=\"code-block\">\u003Cpre>\u003Ccode class=\"hljs language-bash\">pip install 'rembg[cpu,cli]'\u003C\u002Fcode>\u003C\u002Fpre>\u003C\u002Fdiv>\u003Col start=\"2\">\n\u003Cli>Process a single image from the command line\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cdiv class=\"code-block\">\u003Cpre>\u003Ccode class=\"hljs language-bash\">rembg i input.png output.png\u003C\u002Fcode>\u003C\u002Fpre>\u003C\u002Fdiv>\u003Col start=\"3\">\n\u003Cli>Or call it in code\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cdiv class=\"code-block\">\u003Cpre>\u003Ccode class=\"hljs language-python\">from rembg import remove\nfrom PIL import Image\n\ninp = Image.open('input.png')\nremove(inp).save('output.png')\u003C\u002Fcode>\u003C\u002Fpre>\u003C\u002Fdiv>\u003Ch2 id=\"core-advantages\">Core Advantages\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>Local inference\u003C\u002Fstrong>: models run on your machine, images stay inside the network\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Flexible modes\u003C\u002Fstrong>: one capability serves personal scripts through production deployments\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Ready out of the box\u003C\u002Fstrong>: models download automatically on first run, no manual setup\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"system-requirements\">System Requirements\u003C\u002Fh2>\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>Item\u003C\u002Fth>\n\u003Cth>Requirement\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>Language\u003C\u002Ftd>\n\u003Ctd>Python 3.11 up to, but not including, 3.14\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>OS\u003C\u002Ftd>\n\u003Ctd>Linux, macOS, Windows\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Hardware\u003C\u002Ftd>\n\u003Ctd>CPU works; NVIDIA GPU enables acceleration\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Ch2 id=\"notes\">Notes\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>Models download on first run\u003C\u002Fstrong>: the model files are large; pre-place them manually if your network is slow\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Results vary by image\u003C\u002Fstrong>: for subjects close in color to the background or with complex edges, try a different model or enable alpha matting\u003C\u002Fli>\n\u003C\u002Ful>\n",1793197842256]