{"adoption": {"forks": 1303, "observed_at": "2026-08-28T04:11:05.473837+00:00", "stars": 14095}, "canonical_url": "https://ross.abutalabs.com/products/open_clip", "card": {"archived": false, "artifact_type": "library", "description": "An open source implementation of CLIP.", "domain": ["deep-learning", "computer-vision", "machine-learning"], "enriched": true, "function": ["machine-learning", "deep-learning", "computer-vision", "nlp", "llm-training"], "health_score": 96, "homepage": null, "language": "Python", "license": "NOASSERTION", "license_family": "other", "maturity": "active", "member_repos": ["mlfoundations/open_clip"], "name": "mlfoundations/open_clip", "platform": ["python"], "pushed_at": "2026-08-23T20:06:40+00:00", "repo": "mlfoundations/open_clip", "stars": 14095, "tags": ["clip", "contrastive-learning", "pytorch", "multimodal", "zero-shot-classification", "pretrained-models", "image-text", "natural-language-processing", "gpu"], "topics": ["deep-learning", "pytorch", "computer-vision", "language-model", "multi-modal-learning", "contrastive-loss", "zero-shot-classification", "pretrained-models"], "urls": [], "use_cases": ["compute image and text embeddings with pretrained CLIP models", "zero-shot image classification from natural language prompts", "train a custom CLIP model on my own image-text dataset", "build image search by comparing text queries to image embeddings", "fine-tune contrastive image-text models", "extract features from images for downstream tasks"], "what_it_is": "OpenCLIP is an open-source PyTorch implementation of CLIP and related multimodal contrastive models, with many pretrained image/text checkpoints. It supports both inference with pretrained models and large-scale contrastive training including newer architectures like CoCa, MaMMUT, and NaFlex variants.", "when_to_avoid": ["you need a non-PyTorch framework like TensorFlow or JAX", "you only need a hosted inference API without local GPU resources", "your task is single-modality (pure vision or pure NLP) with no cross-modal needs"], "when_to_choose": ["you need CLIP-style multimodal embeddings in PyTorch", "you want zero-shot classification without training a custom classifier", "you need to pretrain or fine-tune contrastive image-text models at scale", "you want access to many open pretrained checkpoints (OpenAI, LAION, DataComp)"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/open_clip", "repo": "mlfoundations/open_clip", "role": "main", "score": 86}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:11:05.473837+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-29T17:12:49.253080+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "9b14e426bd1ecaefba4e440219a8ffef74329140af4aeaa8d17d9e59ae4084aa", "fetched_at": "2026-08-28T04:11:05.473837+00:00", "kind": "readme", "missing": false, "url": "https://github.com/mlfoundations/open_clip"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:11:05.473837+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-29T17:12:49.253080+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "9b14e426bd1ecaefba4e440219a8ffef74329140af4aeaa8d17d9e59ae4084aa", "fetched_at": "2026-08-28T04:11:05.473837+00:00", "kind": "readme", "missing": false, "url": "https://github.com/mlfoundations/open_clip"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-29T17:12:49.253080+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "9b14e426bd1ecaefba4e440219a8ffef74329140af4aeaa8d17d9e59ae4084aa", "fetched_at": "2026-08-28T04:11:05.473837+00:00", "kind": "readme", "missing": false, "url": "https://github.com/mlfoundations/open_clip"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:11:05.473837+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:11:05.473837+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:11:05.473837+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-29T17:12:49.253080+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "9b14e426bd1ecaefba4e440219a8ffef74329140af4aeaa8d17d9e59ae4084aa", "fetched_at": "2026-08-28T04:11:05.473837+00:00", "kind": "readme", "missing": false, "url": "https://github.com/mlfoundations/open_clip"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:11:05.473837+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:11:05.473837+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-29T17:12:49.253080+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "9b14e426bd1ecaefba4e440219a8ffef74329140af4aeaa8d17d9e59ae4084aa", "fetched_at": "2026-08-28T04:11:05.473837+00:00", "kind": "readme", "missing": false, "url": "https://github.com/mlfoundations/open_clip"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:11:05.473837+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:11:05.473837+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:11:05.473837+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-29T17:12:49.253080+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "9b14e426bd1ecaefba4e440219a8ffef74329140af4aeaa8d17d9e59ae4084aa", "fetched_at": "2026-08-28T04:11:05.473837+00:00", "kind": "readme", "missing": false, "url": "https://github.com/mlfoundations/open_clip"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:11:05.473837+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:11:05.473837+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-29T17:12:49.253080+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "9b14e426bd1ecaefba4e440219a8ffef74329140af4aeaa8d17d9e59ae4084aa", "fetched_at": "2026-08-28T04:11:05.473837+00:00", "kind": "readme", "missing": false, "url": "https://github.com/mlfoundations/open_clip"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-29T17:12:49.253080+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "9b14e426bd1ecaefba4e440219a8ffef74329140af4aeaa8d17d9e59ae4084aa", "fetched_at": "2026-08-28T04:11:05.473837+00:00", "kind": "readme", "missing": false, "url": "https://github.com/mlfoundations/open_clip"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-29T17:12:49.253080+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "9b14e426bd1ecaefba4e440219a8ffef74329140af4aeaa8d17d9e59ae4084aa", "fetched_at": "2026-08-28T04:11:05.473837+00:00", "kind": "readme", "missing": false, "url": "https://github.com/mlfoundations/open_clip"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-29T17:12:49.253080+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "9b14e426bd1ecaefba4e440219a8ffef74329140af4aeaa8d17d9e59ae4084aa", "fetched_at": "2026-08-28T04:11:05.473837+00:00", "kind": "readme", "missing": false, "url": "https://github.com/mlfoundations/open_clip"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 99, "longevity": 100, "rhythm": 60}, "computed_at": "2026-09-03T02:20:16.233290+00:00", "flags": ["no_license"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 1862, "days_push": 10, "days_rel": 188, "gap_med": 43.5, "n_releases_24m": 11}, "score": 86, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}