{"schema":"record/v1","id":"art_01M5NBERFW9PGPVDPK07PE3YF9","type":"article","revision":1,"etag":"\"r1.s0\"","title":"What do the Nuke 17.1v2 docs say CopyCat, Inference, Cattery, CatFileCreator and BigCat do for rotoscoping, how should the training data be built, and how do they tell you to fix bad mattes?","summary":"Partially verified: the curator re-read none of the cited sources; S17, S19, S20, S22, S18, S25, S23, S24 rest on the producer's pinned reading. In Foundry's Nuke 17.1v2 documentation CopyCat is a NukeX/Nuke Studio node that trains a per-shot network from a few Input/Ground Truth frame pairs (for example six frames and six hand-made roto masks) and writes a .cat file that the Inference node applies to the whole sequence. The docs call for diverse frames, matched pair order, faked extra pairs for shadows or defocus not in the data set, checkpoints for retraining, and Grade/RotoPaint cleanup of artifacts; they offer a \"Human Matting\" initial-weights option. BigCat is a separate node for generalising across many shots, and the docs say CopyCat deliberately overfits one shot. The pages give no accuracy numbers, and the Cattery page describes a library of third-party models whose listing is JavaScript-only (not read).","status":"active","body":{"text":"Partially verified: the curator re-read none of the cited sources; S17, S19, S20, S22, S18, S25, S23, S24 rest on the producer's pinned reading.\n\nIn Foundry's Nuke 17.1v2 documentation CopyCat is a NukeX/Nuke Studio node that trains a per-shot network from a few Input/Ground Truth frame pairs (for example six frames and six hand-made roto masks) and writes a .cat file that the Inference node applies to the whole sequence. The docs call for diverse frames, matched pair order, faked extra pairs for shadows or defocus not in the data set, checkpoints for retraining, and Grade/RotoPaint cleanup of artifacts; they offer a \"Human Matting\" initial-weights option. BigCat is a separate node for generalising across many shots, and the docs say CopyCat deliberately overfits one shot. The pages give no accuracy numbers, and the Cattery page describes a library of third-party models whose listing is JavaScript-only (not read).\n\nCurator qualifications: Partially verified: the curator re-read none of the cited sources; S17, S19, S20, S22, S18, S25, S23, S24 rest on the producer's pinned reading. Curator GET 2026-10-23 of the sources not re-read (mechanical source evidence, not semantic verification): S17: HTTP 200, bytes equal to the producer pin; S19: HTTP 200, bytes equal to the producer pin; S20: HTTP 200, bytes equal to the producer pin; S22: HTTP 200, bytes equal to the producer pin; S18: HTTP 200, bytes equal to the producer pin; S25: HTTP 200, bytes equal to the producer pin; S23: HTTP 200, bytes equal to the producer pin; S24: HTTP 200, bytes equal to the producer pin.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://learn.foundry.com/nuke/content/reference_guide/air_nodes/copycat.html","note":"intro"},{"url":"https://learn.foundry.com/nuke/content/comp_environment/air_tools/cc-intro.html","note":"intro"},{"url":"https://learn.foundry.com/nuke/content/comp_environment/air_tools/cc-data.html","note":"intro"},{"url":"https://learn.foundry.com/nuke/content/comp_environment/air_tools/cc-apply.html","note":"intro"},{"url":"https://learn.foundry.com/nuke/content/reference_guide/air_nodes/inference.html","note":"intro"},{"url":"https://learn.foundry.com/nuke/content/comp_environment/air_tools/using-bigcat.html","note":"intro"},{"url":"https://learn.foundry.com/nuke/content/comp_environment/air_tools/cattery.html","note":"intro"},{"url":"https://learn.foundry.com/nuke/content/comp_environment/air_tools/cfc-intro.html","note":"intro"}]},"categories":["T11"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"metadata_checked","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://learn.foundry.com/nuke/content/reference_guide/air_nodes/copycat.html","claim":"In Foundry's Nuke 17.1v2 documentation CopyCat is a NukeX/Nuke Studio node that trains a per-shot network from a few Input/Ground Truth frame pairs (for example six frames and six hand-made roto masks) and writes a .cat file that the Inference node applies to the whole sequence. The docs call for diverse frames, matched pair order, faked extra pairs for shadows or defocus not in the data set, checkpoints for retraining, and Grade/RotoPaint cleanup of artifacts; they offer a \"Human Matting\" initial-weights option. BigCat is a separate node for generalising across many shots, and the docs say CopyCat deliberately overfits one shot. The pages give no accuracy numbers, and the Cattery page describes a library of third-party models whose listing is JavaScript-only (not read).","locator":"intro","classification":"sourced_fact"}],"execution_record_ids":[]},"rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"attribution":{"origin":"research_agent","upstream_owner":null,"upstream_url":null,"imported_from":"records/p-0c8f557a58d0fa6827536521f522056ee66087e5/record.json#p-0c8f557a58d0fa6827536521f522056ee66087e5/record","import_batch_id":"pipe-c7fcdac892350af65324cf2f432c00fe31e41297","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-c7fcdac892350af65324cf2f432c00fe31e41297","original":{"body":{"text":"Partially verified: the curator re-read none of the cited sources; S17, S19, S20, S22, S18, S25, S23, S24 rest on the producer's pinned reading.\n\nIn Foundry's Nuke 17.1v2 documentation CopyCat is a NukeX/Nuke Studio node that trains a per-shot network from a few Input/Ground Truth frame pairs (for example six frames and six hand-made roto masks) and writes a .cat file that the Inference node applies to the whole sequence. The docs call for diverse frames, matched pair order, faked extra pairs for shadows or defocus not in the data set, checkpoints for retraining, and Grade/RotoPaint cleanup of artifacts; they offer a \"Human Matting\" initial-weights option. BigCat is a separate node for generalising across many shots, and the docs say CopyCat deliberately overfits one shot. The pages give no accuracy numbers, and the Cattery page describes a library of third-party models whose listing is JavaScript-only (not read).\n\nCurator qualifications: Partially verified: the curator re-read none of the cited sources; S17, S19, S20, S22, S18, S25, S23, S24 rest on the producer's pinned reading. Curator GET 2026-10-23 of the sources not re-read (mechanical source evidence, not semantic verification): S17: HTTP 200, bytes equal to the producer pin; S19: HTTP 200, bytes equal to the producer pin; S20: HTTP 200, bytes equal to the producer pin; S22: HTTP 200, bytes equal to the producer pin; S18: HTTP 200, bytes equal to the producer pin; S25: HTTP 200, bytes equal to the producer pin; S23: HTTP 200, bytes equal to the producer pin; S24: HTTP 200, bytes equal to the producer pin.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://learn.foundry.com/nuke/content/reference_guide/air_nodes/copycat.html","note":"intro"},{"url":"https://learn.foundry.com/nuke/content/comp_environment/air_tools/cc-intro.html","note":"intro"},{"url":"https://learn.foundry.com/nuke/content/comp_environment/air_tools/cc-data.html","note":"intro"},{"url":"https://learn.foundry.com/nuke/content/comp_environment/air_tools/cc-apply.html","note":"intro"},{"url":"https://learn.foundry.com/nuke/content/reference_guide/air_nodes/inference.html","note":"intro"},{"url":"https://learn.foundry.com/nuke/content/comp_environment/air_tools/using-bigcat.html","note":"intro"},{"url":"https://learn.foundry.com/nuke/content/comp_environment/air_tools/cattery.html","note":"intro"},{"url":"https://learn.foundry.com/nuke/content/comp_environment/air_tools/cfc-intro.html","note":"intro"}]},"tags":[],"type":"article","links":[],"title":"What do the Nuke 17.1v2 docs say CopyCat, Inference, Cattery, CatFileCreator and BigCat do for rotoscoping, how should the training data be built, and how do they tell you to fix bad mattes?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"Partially verified: the curator re-read none of the cited sources; S17, S19, S20, S22, S18, S25, S23, S24 rest on the producer's pinned reading. In Foundry's Nuke 17.1v2 documentation CopyCat is a NukeX/Nuke Studio node that trains a per-shot network from a few Input/Ground Truth frame pairs (for example six frames and six hand-made roto masks) and writes a .cat file that the Inference node applies to the whole sequence. The docs call for diverse frames, matched pair order, faked extra pairs for shadows or defocus not in the data set, checkpoints for retraining, and Grade/RotoPaint cleanup of artifacts; they offer a \"Human Matting\" initial-weights option. BigCat is a separate node for generalising across many shots, and the docs say CopyCat deliberately overfits one shot. The pages give no accuracy numbers, and the Cattery page describes a library of third-party models whose listing is JavaScript-only (not read).","evidence":{"claims":[{"url":"https://learn.foundry.com/nuke/content/reference_guide/air_nodes/copycat.html","claim":"In Foundry's Nuke 17.1v2 documentation CopyCat is a NukeX/Nuke Studio node that trains a per-shot network from a few Input/Ground Truth frame pairs (for example six frames and six hand-made roto masks) and writes a .cat file that the Inference node applies to the whole sequence. The docs call for diverse frames, matched pair order, faked extra pairs for shadows or defocus not in the data set, checkpoints for retraining, and Grade/RotoPaint cleanup of artifacts; they offer a \"Human Matting\" initial-weights option. BigCat is a separate node for generalising across many shots, and the docs say CopyCat deliberately overfits one shot. The pages give no accuracy numbers, and the Cattery page describes a library of third-party models whose listing is JavaScript-only (not read).","locator":"intro","classification":"sourced_fact"}],"review":"unreviewed","execution":"not_run","inspection":"metadata_checked","execution_record_ids":[]},"language":"en","categories":["T11"],"extensions":{"curation/v1":{"curator":"CURATOR-A-20261021","revision":1,"topic_ids":["CAT:T11.05"],"confidence":"Not stated; see limitations.","limitations":["Source pin hashes are native catalogue descriptor hashes, not upstream byte hashes. 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