{"adoption": {"forks": 306, "observed_at": "2026-08-28T04:07:33.172209+00:00", "stars": 2982}, "canonical_url": "https://ross.abutalabs.com/products/flops-counterpytorch", "card": {"archived": false, "artifact_type": "library", "description": "Flops counter for neural networks in pytorch framework", "domain": ["deep-learning", "machine-learning", "performance", "developer-tools"], "enriched": true, "function": ["benchmarking", "machine-learning", "deep-learning", "developer-tools"], "health_score": 57, "homepage": null, "language": "Python", "license": "MIT", "license_family": "permissive", "maturity": "active", "member_repos": ["sovrasov/flops-counter.pytorch"], "name": "sovrasov/flops-counter.pytorch", "platform": ["python", "cross-platform"], "pushed_at": "2025-08-20T17:23:29+00:00", "repo": "sovrasov/flops-counter.pytorch", "stars": 2982, "tags": ["flops-counter", "pytorch", "model-complexity", "ptflops", "neural-networks", "transformers", "cnn", "parameter-counting"], "topics": ["pytorch", "pytorch-cnn", "deep-neural-networks", "deeplearning", "pytorch-utils", "flops-counter", "transformer", "transformer-models"], "urls": [], "use_cases": ["count FLOPs of a PyTorch model", "estimate computational complexity of a CNN or transformer", "count the number of parameters in a neural network", "get per-layer computational cost breakdown of a model", "compare efficiency of model architectures", "measure inference cost of a vision transformer", "profile model complexity for a research paper"], "what_it_is": "A Python library (ptflops) that computes the theoretical number of multiply-add operations (FLOPs) and parameter counts for neural network models in PyTorch. It supports two backends (aten and pytorch) and can print per-layer computational cost breakdowns.", "when_to_avoid": ["you need runtime latency or actual measured speed rather than theoretical operation counts", "you work outside PyTorch (e.g., TensorFlow, JAX)", "you need GPU memory profiling or training-time cost estimation"], "when_to_choose": ["you need theoretical FLOPs or MACs estimates for PyTorch models including transformers", "you want per-layer cost analytics for CNNs", "you need a lightweight pip-installable complexity counter with MIT license"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/flops-counterpytorch", "repo": "sovrasov/flops-counter.pytorch", "role": "main", "score": 41}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:07:33.172209+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T07:31:28.697920+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "3dd494fe3dd67b0fa200940033abaf4e5e9561f2328d4e62bc56589a87780825", "fetched_at": "2026-08-28T04:07:33.172209+00:00", "kind": "readme", "missing": false, "url": "https://github.com/sovrasov/flops-counter.pytorch"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:07:33.172209+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T07:31:28.697920+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "3dd494fe3dd67b0fa200940033abaf4e5e9561f2328d4e62bc56589a87780825", "fetched_at": "2026-08-28T04:07:33.172209+00:00", "kind": "readme", "missing": false, "url": "https://github.com/sovrasov/flops-counter.pytorch"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T07:31:28.697920+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "3dd494fe3dd67b0fa200940033abaf4e5e9561f2328d4e62bc56589a87780825", "fetched_at": "2026-08-28T04:07:33.172209+00:00", "kind": "readme", "missing": false, "url": "https://github.com/sovrasov/flops-counter.pytorch"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:07:33.172209+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:07:33.172209+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:07:33.172209+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T07:31:28.697920+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "3dd494fe3dd67b0fa200940033abaf4e5e9561f2328d4e62bc56589a87780825", "fetched_at": "2026-08-28T04:07:33.172209+00:00", "kind": "readme", "missing": false, "url": "https://github.com/sovrasov/flops-counter.pytorch"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:07:33.172209+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:07:33.172209+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T07:31:28.697920+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "3dd494fe3dd67b0fa200940033abaf4e5e9561f2328d4e62bc56589a87780825", "fetched_at": "2026-08-28T04:07:33.172209+00:00", "kind": "readme", "missing": false, "url": "https://github.com/sovrasov/flops-counter.pytorch"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:07:33.172209+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:07:33.172209+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:07:33.172209+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T07:31:28.697920+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "3dd494fe3dd67b0fa200940033abaf4e5e9561f2328d4e62bc56589a87780825", "fetched_at": "2026-08-28T04:07:33.172209+00:00", "kind": "readme", "missing": false, "url": "https://github.com/sovrasov/flops-counter.pytorch"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:07:33.172209+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:07:33.172209+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T07:31:28.697920+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "3dd494fe3dd67b0fa200940033abaf4e5e9561f2328d4e62bc56589a87780825", "fetched_at": "2026-08-28T04:07:33.172209+00:00", "kind": "readme", "missing": false, "url": "https://github.com/sovrasov/flops-counter.pytorch"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T07:31:28.697920+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "3dd494fe3dd67b0fa200940033abaf4e5e9561f2328d4e62bc56589a87780825", "fetched_at": "2026-08-28T04:07:33.172209+00:00", "kind": "readme", "missing": false, "url": "https://github.com/sovrasov/flops-counter.pytorch"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T07:31:28.697920+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "3dd494fe3dd67b0fa200940033abaf4e5e9561f2328d4e62bc56589a87780825", "fetched_at": "2026-08-28T04:07:33.172209+00:00", "kind": "readme", "missing": false, "url": "https://github.com/sovrasov/flops-counter.pytorch"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T07:31:28.697920+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "3dd494fe3dd67b0fa200940033abaf4e5e9561f2328d4e62bc56589a87780825", "fetched_at": "2026-08-28T04:07:33.172209+00:00", "kind": "readme", "missing": false, "url": "https://github.com/sovrasov/flops-counter.pytorch"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 37, "longevity": 100, "rhythm": 12}, "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": 2938, "days_push": 378, "days_rel": 378, "gap_med": null, "n_releases_24m": 1}, "score": 41, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}