{"adoption": {"forks": 166, "observed_at": "2026-08-28T04:03:46.137568+00:00", "stars": 1148}, "canonical_url": "https://ross.abutalabs.com/products/watermark-removal-pytorch", "card": {"archived": false, "artifact_type": "library", "description": "🔥 CNN for Watermark Removal using Deep Image Prior with Pytorch 🔥.", "domain": ["computer-vision", "image-processing", "deep-learning"], "enriched": true, "function": ["image-processing", "machine-learning", "deep-learning"], "health_score": 20, "homepage": null, "language": "Jupyter Notebook", "license": "MIT", "license_family": "permissive", "maturity": "maintenance", "member_repos": ["braindotai/Watermark-Removal-Pytorch"], "name": "braindotai/Watermark-Removal-Pytorch", "platform": ["python", "cross-platform"], "pushed_at": "2024-10-15T09:45:41+00:00", "repo": "braindotai/Watermark-Removal-Pytorch", "stars": 1148, "tags": ["watermark-removal", "deep-image-prior", "pytorch", "image-restoration", "inpainting", "gpu"], "topics": ["deep-image-priors", "watermark-removal", "pytorch", "watermark", "skip-connections", "artefacts-removal", "paper", "restoration-tasks", "deep-learning"], "urls": [], "use_cases": ["remove watermarks from images", "restore images with known watermark masks", "remove artefacts from photos without training a model", "run deep image prior image restoration in pytorch", "clean watermarked images when the watermark overlay is available"], "what_it_is": "A PyTorch implementation of watermark removal based on the Deep Image Prior paper, using a CNN generator's structure to restore images without training data. It includes a simple API wrapper and supports CUDA and MPS devices.", "when_to_avoid": ["you need to remove watermarks without any knowledge of the watermark or its position", "you need batch processing of thousands of images at production scale", "you need a general-purpose inpainting or photo editing suite"], "when_to_choose": ["you have the watermark (or mask) matching the image's position and scale", "you want training-free image restoration with realistic results", "you want a lightweight model with fast inference on GPU or Apple MPS"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/watermark-removal-pytorch", "repo": "braindotai/Watermark-Removal-Pytorch", "role": "main", "score": 23}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:03:46.137568+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T06:33:49.463903+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "21d2b66673bbd9b35b85c190fee8f4d6e981a773e6fe5e08f849a6b517364c95", "fetched_at": "2026-08-28T04:03:46.137568+00:00", "kind": "readme", "missing": false, "url": "https://github.com/braindotai/Watermark-Removal-Pytorch"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:03:46.137568+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T06:33:49.463903+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "21d2b66673bbd9b35b85c190fee8f4d6e981a773e6fe5e08f849a6b517364c95", "fetched_at": "2026-08-28T04:03:46.137568+00:00", "kind": "readme", "missing": false, "url": "https://github.com/braindotai/Watermark-Removal-Pytorch"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T06:33:49.463903+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "21d2b66673bbd9b35b85c190fee8f4d6e981a773e6fe5e08f849a6b517364c95", "fetched_at": "2026-08-28T04:03:46.137568+00:00", "kind": "readme", "missing": false, "url": "https://github.com/braindotai/Watermark-Removal-Pytorch"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:03:46.137568+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:03:46.137568+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:03:46.137568+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T06:33:49.463903+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "21d2b66673bbd9b35b85c190fee8f4d6e981a773e6fe5e08f849a6b517364c95", "fetched_at": "2026-08-28T04:03:46.137568+00:00", "kind": "readme", "missing": false, "url": "https://github.com/braindotai/Watermark-Removal-Pytorch"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:03:46.137568+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:03:46.137568+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T06:33:49.463903+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "21d2b66673bbd9b35b85c190fee8f4d6e981a773e6fe5e08f849a6b517364c95", "fetched_at": "2026-08-28T04:03:46.137568+00:00", "kind": "readme", "missing": false, "url": "https://github.com/braindotai/Watermark-Removal-Pytorch"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:03:46.137568+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:03:46.137568+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:03:46.137568+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T06:33:49.463903+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "21d2b66673bbd9b35b85c190fee8f4d6e981a773e6fe5e08f849a6b517364c95", "fetched_at": "2026-08-28T04:03:46.137568+00:00", "kind": "readme", "missing": false, "url": "https://github.com/braindotai/Watermark-Removal-Pytorch"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:03:46.137568+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:03:46.137568+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T06:33:49.463903+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "21d2b66673bbd9b35b85c190fee8f4d6e981a773e6fe5e08f849a6b517364c95", "fetched_at": "2026-08-28T04:03:46.137568+00:00", "kind": "readme", "missing": false, "url": "https://github.com/braindotai/Watermark-Removal-Pytorch"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T06:33:49.463903+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "21d2b66673bbd9b35b85c190fee8f4d6e981a773e6fe5e08f849a6b517364c95", "fetched_at": "2026-08-28T04:03:46.137568+00:00", "kind": "readme", "missing": false, "url": "https://github.com/braindotai/Watermark-Removal-Pytorch"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T06:33:49.463903+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "21d2b66673bbd9b35b85c190fee8f4d6e981a773e6fe5e08f849a6b517364c95", "fetched_at": "2026-08-28T04:03:46.137568+00:00", "kind": "readme", "missing": false, "url": "https://github.com/braindotai/Watermark-Removal-Pytorch"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T06:33:49.463903+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "21d2b66673bbd9b35b85c190fee8f4d6e981a773e6fe5e08f849a6b517364c95", "fetched_at": "2026-08-28T04:03:46.137568+00:00", "kind": "readme", "missing": false, "url": "https://github.com/braindotai/Watermark-Removal-Pytorch"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 0, "longevity": 100, "rhythm": 8}, "computed_at": "2026-09-02T17:46:02.011165+00:00", "flags": [], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 2104, "days_push": 687, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 23, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}