{"adoption": {"forks": 1118, "observed_at": "2026-08-28T04:09:41.016575+00:00", "stars": 6252}, "canonical_url": "https://ross.abutalabs.com/products/nanodet", "card": {"archived": false, "artifact_type": "library", "description": "NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥", "domain": ["deep-learning", "computer-vision", "image-processing", "mobile-development"], "enriched": true, "function": ["machine-learning", "deep-learning", "computer-vision", "image-processing"], "health_score": 20, "homepage": null, "language": "Python", "license": "Apache-2.0", "license_family": "permissive", "maturity": "stable", "member_repos": ["RangiLyu/nanodet"], "name": "RangiLyu/nanodet", "platform": ["python", "cpp", "cross-platform"], "pushed_at": "2024-08-08T02:52:55+00:00", "repo": "RangiLyu/nanodet", "stars": 6252, "tags": ["object-detection", "anchor-free", "lightweight-models", "edge-deployment", "ncnn", "on-device-inference", "pytorch", "model-zoo", "real-time-inference", "android", "gpu"], "topics": ["deep-neural-networks", "deep-learning", "object-detection", "anchor-free", "ncnn", "shufflenet", "pytorch", "mnn", "repvgg", "openvino", "efficientnet", "android", "model-zoo", "nanodet-plus", "nanodet"], "urls": [], "use_cases": ["run real-time object detection on mobile devices", "deploy a tiny object detection model on edge hardware", "train a lightweight anchor-free detector on limited GPU memory", "detect objects in images with a small footprint model", "convert a detection model to ncnn or OpenVINO for deployment", "build an Android app with on-device object detection", "benchmark lightweight object detection models on COCO"], "what_it_is": "NanoDet-Plus is a super fast, lightweight anchor-free object detection model implemented in PyTorch, with model sizes as small as 980KB (INT8) and real-time performance on mobile ARM CPUs. It supports easy deployment to multiple inference backends including ncnn, MNN, and OpenVINO, with an Android demo included.", "when_to_avoid": ["you need maximum accuracy and can afford large models like YOLO-X or DINO", "you need segmentation, keypoints, or other tasks beyond bounding-box detection", "you only run inference on high-end servers where model size doesn't matter", "you need frequent updates or active community support"], "when_to_choose": ["you need real-time object detection on mobile or embedded ARM CPUs", "model size and latency are critical constraints", "you want an easy-to-deploy detector with ncnn/MNN/OpenVINO support", "you have limited GPU memory for training a detection model"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/nanodet", "repo": "RangiLyu/nanodet", "role": "main", "score": 23}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:09:41.016575+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-29T17:46:36.121925+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "3a0aa71b2c994128e3ee749d2ac88d5af795223b4346b6311f14278221ba3e3a", "fetched_at": "2026-08-28T04:09:41.016575+00:00", "kind": "readme", "missing": false, "url": "https://github.com/RangiLyu/nanodet"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:09:41.016575+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-29T17:46:36.121925+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "3a0aa71b2c994128e3ee749d2ac88d5af795223b4346b6311f14278221ba3e3a", "fetched_at": "2026-08-28T04:09:41.016575+00:00", "kind": "readme", "missing": false, "url": "https://github.com/RangiLyu/nanodet"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-29T17:46:36.121925+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "3a0aa71b2c994128e3ee749d2ac88d5af795223b4346b6311f14278221ba3e3a", "fetched_at": "2026-08-28T04:09:41.016575+00:00", "kind": "readme", "missing": false, "url": "https://github.com/RangiLyu/nanodet"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:09:41.016575+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:09:41.016575+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:09:41.016575+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-29T17:46:36.121925+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "3a0aa71b2c994128e3ee749d2ac88d5af795223b4346b6311f14278221ba3e3a", "fetched_at": "2026-08-28T04:09:41.016575+00:00", "kind": "readme", "missing": false, "url": "https://github.com/RangiLyu/nanodet"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:09:41.016575+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:09:41.016575+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-29T17:46:36.121925+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "3a0aa71b2c994128e3ee749d2ac88d5af795223b4346b6311f14278221ba3e3a", "fetched_at": "2026-08-28T04:09:41.016575+00:00", "kind": "readme", "missing": false, "url": "https://github.com/RangiLyu/nanodet"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:09:41.016575+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:09:41.016575+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:09:41.016575+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-29T17:46:36.121925+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "3a0aa71b2c994128e3ee749d2ac88d5af795223b4346b6311f14278221ba3e3a", "fetched_at": "2026-08-28T04:09:41.016575+00:00", "kind": "readme", "missing": false, "url": "https://github.com/RangiLyu/nanodet"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:09:41.016575+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:09:41.016575+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-29T17:46:36.121925+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "3a0aa71b2c994128e3ee749d2ac88d5af795223b4346b6311f14278221ba3e3a", "fetched_at": "2026-08-28T04:09:41.016575+00:00", "kind": "readme", "missing": false, "url": "https://github.com/RangiLyu/nanodet"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-29T17:46:36.121925+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "3a0aa71b2c994128e3ee749d2ac88d5af795223b4346b6311f14278221ba3e3a", "fetched_at": "2026-08-28T04:09:41.016575+00:00", "kind": "readme", "missing": false, "url": "https://github.com/RangiLyu/nanodet"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-29T17:46:36.121925+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "3a0aa71b2c994128e3ee749d2ac88d5af795223b4346b6311f14278221ba3e3a", "fetched_at": "2026-08-28T04:09:41.016575+00:00", "kind": "readme", "missing": false, "url": "https://github.com/RangiLyu/nanodet"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-29T17:46:36.121925+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "3a0aa71b2c994128e3ee749d2ac88d5af795223b4346b6311f14278221ba3e3a", "fetched_at": "2026-08-28T04:09:41.016575+00:00", "kind": "readme", "missing": false, "url": "https://github.com/RangiLyu/nanodet"}], "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": 2144, "days_push": 755, "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}}