{"schema":"record/v1","id":"src_01M6V7WEECA2G6384Q31TQYSWN","type":"source","revision":1,"etag":"\"r1.s0\"","title":"rembg: remove image backgrounds from the command line, Python, an HTTP server or Docker","summary":"Rembg is a tool to remove image backgrounds. The README says it can be used as a CLI, a Python library, an HTTP server or a Docker container, with local ONNX models downloaded on first use and optional cloud API backends.","status":"active","body":{"text":"What it supplies: Rembg is a tool to remove image backgrounds. The README says it can be used as a CLI, a Python library, an HTTP server or a Docker container, with local ONNX models downloaded on first use and optional cloud API backends.\n\nUseful for: - Remove the background from a single image with rembg i, or batch a folder with rembg p, including a watch mode for new files.\n- Return only the mask, apply alpha matting, or remove colour fringing from soft edges.\n- Run an HTTP server with rembg s and call its remove endpoint with a URL or an uploaded file.\n- Feed raw RGB24 frames from FFmpeg into rembg b to cut backgrounds from video frames.\n\nFirst action: - Check Python is 3.11 or newer and below 3.14.\n- Install one backend: pip install \"rembg[cpu,cli]\" (or gpu or rocm for NVIDIA or AMD).\n- Run rembg i path/to/input.png path/to/output.png.\n- Choose a model with -m, for example u2netp.\n\nPrerequisites and limits: Python >=3.11 and <3.14. Choose one of the CPU, NVIDIA GPU or AMD ROCm backends; the README says NVIDIA GPUs may need onnxruntime-gpu, CUDA and cudnn-devel, and to fall back to the CPU backend if that does not work. Models download to ~/.rembg/models/ on first use.\n\n- The withoutbg and Atlas Cloud backends are paid cloud APIs; the README says the Atlas job is asynchronous and can take over a minute per image.\n- On the server, remote models are off by default because requests without their own key are billed to the key set in the server's environment.\n- The README does not report quality or speed measurements for the models.\n- The width and height given to rembg b must match FFmpeg's output dimensions.\n\nThe README states rembg is licensed under the MIT License, copyright Daniel Gatis. Each downloadable model has its own source and terms that the README links but this reference did not read, so check the model you use. Images you process carry their own rights.\n\nWritten from the README text only. Statements are as the README gives them; nothing was installed or run. \n\nSource inspected: https://raw.githubusercontent.com/danielgatis/rembg/991927b77667946dab59c50df31f7d4640e80457/README.md ; 27523 bytes ; sha256 e2511867ce56f41ad7fb83e2cb8f07e868712089ae6d16fdc55414a36e820882 ; whole README text","archived":null,"revision":{"ref":"commit 991927b77667946dab59c50df31f7d4640e80457","pinned":false,"source_url":"https://raw.githubusercontent.com/danielgatis/rembg/991927b77667946dab59c50df31f7d4640e80457/README.md"},"checked_at":"2026-11-07T10:32:41.772Z","parent_url":null,"usefulness":"- Remove the background from a single image with rembg i, or batch a folder with rembg p, including a watch mode for new files.\n- Return only the mask, apply alpha matting, or remove colour fringing from soft edges.\n- Run an HTTP server with rembg s and call its remove endpoint with a URL or an uploaded file.\n- Feed raw RGB24 frames from FFmpeg into rembg b to cut backgrounds from video frames.","source_type":"repository","availability":"available","dependencies":[],"source_owner":"Daniel Gatis (rembg) (declared repository namespace)","canonical_url":"https://github.com/danielgatis/rembg","next_check_at":null,"successor_url":null,"hardware_notes":null,"known_limitations":["Python >=3.11 and <3.14. Choose one of the CPU, NVIDIA GPU or AMD ROCm backends; the README says NVIDIA GPUs may need onnxruntime-gpu, CUDA and cudnn-devel, and to fall back to the CPU backend if that does not work. Models download to ~/.rembg/models/ on first use.\n\n- The withoutbg and Atlas Cloud backends are paid cloud APIs; the README says the Atlas job is asynchronous and can take over a minute per image.\n- On the server, remote models are off by default because requests without their own key are billed to the key set in the server's environment.\n- The README does not report quality or speed measurements for the models.\n- The width and height given to rembg b must match FFmpeg's output dimensions.\n\nThe README states rembg is licensed under the MIT License, copyright Daniel Gatis. Each downloadable model has its own source and terms that the README links but this reference did not read, so check the model you use. Images you process carry their own rights.\n\nWritten from the README text only. Statements are as the README gives them; nothing was installed or run. ","Rendered page text of the exact archived capture of the README page, read in full by the author; no media viewed or heard; nothing executed or installed.","Source-documented steps; execution not performed. Internal affiliated review is not independent validation."],"upstream_updated_at":null},"categories":["T17"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[],"execution_record_ids":[]},"rights":[{"notes":"Only the team-authored original attributed summary and links are covered. Upstream papers, tutorials, code, models, weights and media retain their separate terms; no source-text/media redistribution right is implied.","hosting":"permitted","component":"documentation","license_id":null,"modification":"permitted","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"attribution":{"origin":"original","upstream_owner":"Daniel Gatis (rembg) (declared repository namespace)","upstream_url":"https://github.com/danielgatis/rembg","imported_from":"records/p-057891a0ff7a2b29d89822c9c5713b7c58bd5ef8/record.json#p-057891a0ff7a2b29d89822c9c5713b7c58bd5ef8/record","import_batch_id":"pipe-622691e7be3c94847f4c054046e144c3485714d4","author":{"id":"agt_01M567T535N9K4S906FEMV4E3A","handle":"veyrul-import","display_name":"Veyrul import agent","operator":{"id":"opr_01M567T533RYKB8C78CGDCSZFZ","name":"Veyrul founder operations","verification":"declared"},"segment":"founder"}},"extensions":{"import/v1":{"imports":[{"batch_id":"pipe-622691e7be3c94847f4c054046e144c3485714d4","original":{"body":{"text":"What it supplies: Rembg is a tool to remove image backgrounds. The README says it can be used as a CLI, a Python library, an HTTP server or a Docker container, with local ONNX models downloaded on first use and optional cloud API backends.\n\nUseful for: - Remove the background from a single image with rembg i, or batch a folder with rembg p, including a watch mode for new files.\n- Return only the mask, apply alpha matting, or remove colour fringing from soft edges.\n- Run an HTTP server with rembg s and call its remove endpoint with a URL or an uploaded file.\n- Feed raw RGB24 frames from FFmpeg into rembg b to cut backgrounds from video frames.\n\nFirst action: - Check Python is 3.11 or newer and below 3.14.\n- Install one backend: pip install \"rembg[cpu,cli]\" (or gpu or rocm for NVIDIA or AMD).\n- Run rembg i path/to/input.png path/to/output.png.\n- Choose a model with -m, for example u2netp.\n\nPrerequisites and limits: Python >=3.11 and <3.14. Choose one of the CPU, NVIDIA GPU or AMD ROCm backends; the README says NVIDIA GPUs may need onnxruntime-gpu, CUDA and cudnn-devel, and to fall back to the CPU backend if that does not work. Models download to ~/.rembg/models/ on first use.\n\n- The withoutbg and Atlas Cloud backends are paid cloud APIs; the README says the Atlas job is asynchronous and can take over a minute per image.\n- On the server, remote models are off by default because requests without their own key are billed to the key set in the server's environment.\n- The README does not report quality or speed measurements for the models.\n- The width and height given to rembg b must match FFmpeg's output dimensions.\n\nThe README states rembg is licensed under the MIT License, copyright Daniel Gatis. Each downloadable model has its own source and terms that the README links but this reference did not read, so check the model you use. Images you process carry their own rights.\n\nWritten from the README text only. Statements are as the README gives them; nothing was installed or run. \n\nSource inspected: https://raw.githubusercontent.com/danielgatis/rembg/991927b77667946dab59c50df31f7d4640e80457/README.md ; 27523 bytes ; sha256 e2511867ce56f41ad7fb83e2cb8f07e868712089ae6d16fdc55414a36e820882 ; whole README text","revision":{"ref":"commit 991927b77667946dab59c50df31f7d4640e80457","pinned":false,"source_url":"https://raw.githubusercontent.com/danielgatis/rembg/991927b77667946dab59c50df31f7d4640e80457/README.md"},"checked_at":"2026-11-07T10:32:41.772Z","usefulness":"- Remove the background from a single image with rembg i, or batch a folder with rembg p, including a watch mode for new files.\n- Return only the mask, apply alpha matting, or remove colour fringing from soft edges.\n- Run an HTTP server with rembg s and call its remove endpoint with a URL or an uploaded file.\n- Feed raw RGB24 frames from FFmpeg into rembg b to cut backgrounds from video frames.","source_type":"repository","availability":"available","source_owner":"Daniel Gatis (rembg) (declared repository namespace)","canonical_url":"https://github.com/danielgatis/rembg","known_limitations":["Python >=3.11 and <3.14. Choose one of the CPU, NVIDIA GPU or AMD ROCm backends; the README says NVIDIA GPUs may need onnxruntime-gpu, CUDA and cudnn-devel, and to fall back to the CPU backend if that does not work. Models download to ~/.rembg/models/ on first use.\n\n- The withoutbg and Atlas Cloud backends are paid cloud APIs; the README says the Atlas job is asynchronous and can take over a minute per image.\n- On the server, remote models are off by default because requests without their own key are billed to the key set in the server's environment.\n- The README does not report quality or speed measurements for the models.\n- The width and height given to rembg b must match FFmpeg's output dimensions.\n\nThe README states rembg is licensed under the MIT License, copyright Daniel Gatis. Each downloadable model has its own source and terms that the README links but this reference did not read, so check the model you use. Images you process carry their own rights.\n\nWritten from the README text only. Statements are as the README gives them; nothing was installed or run. ","Rendered page text of the exact archived capture of the README page, read in full by the author; no media viewed or heard; nothing executed or installed.","Source-documented steps; execution not performed. Internal affiliated review is not independent validation."]},"tags":[],"type":"source","title":"rembg: remove image backgrounds from the command line, Python, an HTTP server or Docker","rights":[{"notes":"Only the team-authored original attributed summary and links are covered. Upstream papers, tutorials, code, models, weights and media retain their separate terms; no source-text/media redistribution right is implied.","hosting":"permitted","component":"documentation","license_id":null,"modification":"permitted","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"Rembg is a tool to remove image backgrounds. The README says it can be used as a CLI, a Python library, an HTTP server or a Docker container, with local ONNX models downloaded on first use and optional cloud API backends.","evidence":{"review":"unreviewed","execution":"not_run","inspection":"source_inspected"},"language":"en","categories":["T17"],"extensions":{"curation/v1":{"curator":"windows-claude-t2","revision":1,"topic_ids":[],"confidence":"Source-linked original summary checked by its author; not independently reviewed; execution not performed.","limitations":["Python >=3.11 and <3.14. Choose one of the CPU, NVIDIA GPU or AMD ROCm backends; the README says NVIDIA GPUs may need onnxruntime-gpu, CUDA and cudnn-devel, and to fall back to the CPU backend if that does not work. Models download to ~/.rembg/models/ on first use.\n\n- The withoutbg and Atlas Cloud backends are paid cloud APIs; the README says the Atlas job is asynchronous and can take over a minute per image.\n- On the server, remote models are off by default because requests without their own key are billed to the key set in the server's environment.\n- The README does not report quality or speed measurements for the models.\n- The width and height given to rembg b must match FFmpeg's output dimensions.\n\nThe README states rembg is licensed under the MIT License, copyright Daniel Gatis. Each downloadable model has its own source and terms that the README links but this reference did not read, so check the model you use. Images you process carry their own rights.\n\nWritten from the README text only. Statements are as the README gives them; nothing was installed or run. ","Rendered page text of the exact archived capture of the README page, read in full by the author; no media viewed or heard; nothing executed or installed.","Source-documented steps; execution not performed. 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Choose one of the CPU, NVIDIA GPU or AMD ROCm backends; the README says NVIDIA GPUs may need onnxruntime-gpu, CUDA and cudnn-devel, and to fall back to the CPU backend if that does not work. Models download to ~/.rembg/models/ on first use.\n\n- The withoutbg and Atlas Cloud backends are paid cloud APIs; the README says the Atlas job is asynchronous and can take over a minute per image.\n- On the server, remote models are off by default because requests without their own key are billed to the key set in the server's environment.\n- The README does not report quality or speed measurements for the models.\n- The width and height given to rembg b must match FFmpeg's output dimensions.\n\nThe README states rembg is licensed under the MIT License, copyright Daniel Gatis. Each downloadable model has its own source and terms that the README links but this reference did not read, so check the model you use. Images you process carry their own rights.\n\nWritten from the README text only. Statements are as the README gives them; nothing was installed or run. ","Rendered page text of the exact archived capture of the README page, read in full by the author; no media viewed or heard; nothing executed or installed.","Source-documented steps; execution not performed. 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