{"adoption": {"forks": 543, "observed_at": "2026-08-28T04:05:37.164468+00:00", "stars": 1792}, "canonical_url": "https://ross.abutalabs.com/products/tensorrt_demos", "card": {"archived": false, "artifact_type": "library", "description": "TensorRT MODNet, YOLOv4, YOLOv3, SSD, MTCNN, and GoogLeNet", "domain": ["deep-learning", "computer-vision", "gpu-computing", "embedded-systems", "developer-tools"], "enriched": true, "function": ["machine-learning", "image-processing", "computer-vision", "gpu-computing", "developer-tools"], "health_score": 44, "homepage": "https://jkjung-avt.github.io/", "language": "Python", "license": "MIT", "license_family": "permissive", "maturity": "maintenance", "member_repos": ["jkjung-avt/tensorrt_demos"], "name": "jkjung-avt/tensorrt_demos", "platform": ["python", "embedded"], "pushed_at": "2025-09-02T01:43:26+00:00", "repo": "jkjung-avt/tensorrt_demos", "stars": 1792, "tags": ["tensorrt", "jetson", "yolov4", "yolov3", "ssd-mobilenet", "mtcnn", "googlenet", "modnet", "object-detection", "model-optimization", "inference", "linux", "gpu"], "topics": ["tensorrt", "yolov4", "yolov3", "ssd-mobilenet", "mtcnn", "googlenet", "modnet", "object-detection", "jetson"], "urls": [], "use_cases": ["run yolov4 object detection on jetson nano with tensorrt", "optimize deep learning models with tensorrt for jetson", "run face detection mtcnn on jetson xavier nx", "video matting with modnet on jetson", "benchmark ssd mobilenet inference speed on jetson", "run tensorrt inference on x86 gpu", "convert darknet or caffe models to tensorrt engines"], "what_it_is": "A collection of Python demo programs showing how to optimize and run deep learning models (YOLOv4, YOLOv3, SSD, MTCNN, GoogLeNet, MODNet) with NVIDIA TensorRT. It targets NVIDIA Jetson developer kits and x86_64 PCs with NVIDIA GPUs, providing optimized inference examples with benchmarked performance.", "when_to_avoid": ["you need a production-ready inference framework rather than demo examples", "you target non-NVIDIA hardware or CPUs", "you need models not covered by the demos (e.g., transformers or segmentation models)", "you want training code rather than inference"], "when_to_choose": ["you need fast optimized inference of these specific models on NVIDIA Jetson hardware", "you want reference code for converting Caffe/TensorFlow/DarkNet/PyTorch models to TensorRT", "you are benchmarking object detection performance on embedded NVIDIA devices"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/tensorrt_demos", "repo": "jkjung-avt/tensorrt_demos", "role": "main", "score": 50}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:05:37.164468+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T03:22:58.411314+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "5f34ced225326a1a352be1d35486f6551ebcc6f0dc3cc032932e8cdd2eb221be", "fetched_at": "2026-08-28T04:05:37.164468+00:00", "kind": "readme", "missing": false, "url": "https://github.com/jkjung-avt/tensorrt_demos"}, {"content_hash": "4701350547bfcce64119e646206ebcf1605f54dc99683309d7e2eb38c9664a1d", "fetched_at": "2026-08-29T11:01:58.312334+00:00", "kind": "homepage", "missing": false, "url": "https://jkjung-avt.github.io/"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:05:37.164468+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T03:22:58.411314+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "5f34ced225326a1a352be1d35486f6551ebcc6f0dc3cc032932e8cdd2eb221be", "fetched_at": "2026-08-28T04:05:37.164468+00:00", "kind": "readme", "missing": false, "url": "https://github.com/jkjung-avt/tensorrt_demos"}, {"content_hash": "4701350547bfcce64119e646206ebcf1605f54dc99683309d7e2eb38c9664a1d", "fetched_at": "2026-08-29T11:01:58.312334+00:00", "kind": "homepage", "missing": false, "url": "https://jkjung-avt.github.io/"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T03:22:58.411314+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "5f34ced225326a1a352be1d35486f6551ebcc6f0dc3cc032932e8cdd2eb221be", "fetched_at": "2026-08-28T04:05:37.164468+00:00", "kind": "readme", "missing": false, "url": "https://github.com/jkjung-avt/tensorrt_demos"}, {"content_hash": "4701350547bfcce64119e646206ebcf1605f54dc99683309d7e2eb38c9664a1d", "fetched_at": "2026-08-29T11:01:58.312334+00:00", "kind": "homepage", "missing": false, "url": "https://jkjung-avt.github.io/"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:05:37.164468+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:05:37.164468+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:05:37.164468+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T03:22:58.411314+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "5f34ced225326a1a352be1d35486f6551ebcc6f0dc3cc032932e8cdd2eb221be", "fetched_at": "2026-08-28T04:05:37.164468+00:00", "kind": "readme", "missing": false, "url": "https://github.com/jkjung-avt/tensorrt_demos"}, {"content_hash": "4701350547bfcce64119e646206ebcf1605f54dc99683309d7e2eb38c9664a1d", "fetched_at": "2026-08-29T11:01:58.312334+00:00", "kind": "homepage", "missing": false, "url": "https://jkjung-avt.github.io/"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:05:37.164468+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:05:37.164468+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T03:22:58.411314+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "5f34ced225326a1a352be1d35486f6551ebcc6f0dc3cc032932e8cdd2eb221be", "fetched_at": "2026-08-28T04:05:37.164468+00:00", "kind": "readme", "missing": false, "url": "https://github.com/jkjung-avt/tensorrt_demos"}, {"content_hash": "4701350547bfcce64119e646206ebcf1605f54dc99683309d7e2eb38c9664a1d", "fetched_at": "2026-08-29T11:01:58.312334+00:00", "kind": "homepage", "missing": false, "url": "https://jkjung-avt.github.io/"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:05:37.164468+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:05:37.164468+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:05:37.164468+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T03:22:58.411314+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "5f34ced225326a1a352be1d35486f6551ebcc6f0dc3cc032932e8cdd2eb221be", "fetched_at": "2026-08-28T04:05:37.164468+00:00", "kind": "readme", "missing": false, "url": "https://github.com/jkjung-avt/tensorrt_demos"}, {"content_hash": "4701350547bfcce64119e646206ebcf1605f54dc99683309d7e2eb38c9664a1d", "fetched_at": "2026-08-29T11:01:58.312334+00:00", "kind": "homepage", "missing": false, "url": "https://jkjung-avt.github.io/"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:05:37.164468+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:05:37.164468+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T03:22:58.411314+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "5f34ced225326a1a352be1d35486f6551ebcc6f0dc3cc032932e8cdd2eb221be", "fetched_at": "2026-08-28T04:05:37.164468+00:00", "kind": "readme", "missing": false, "url": "https://github.com/jkjung-avt/tensorrt_demos"}, {"content_hash": "4701350547bfcce64119e646206ebcf1605f54dc99683309d7e2eb38c9664a1d", "fetched_at": "2026-08-29T11:01:58.312334+00:00", "kind": "homepage", "missing": false, "url": "https://jkjung-avt.github.io/"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T03:22:58.411314+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "5f34ced225326a1a352be1d35486f6551ebcc6f0dc3cc032932e8cdd2eb221be", "fetched_at": "2026-08-28T04:05:37.164468+00:00", "kind": "readme", "missing": false, "url": "https://github.com/jkjung-avt/tensorrt_demos"}, {"content_hash": "4701350547bfcce64119e646206ebcf1605f54dc99683309d7e2eb38c9664a1d", "fetched_at": "2026-08-29T11:01:58.312334+00:00", "kind": "homepage", "missing": false, "url": "https://jkjung-avt.github.io/"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T03:22:58.411314+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "5f34ced225326a1a352be1d35486f6551ebcc6f0dc3cc032932e8cdd2eb221be", "fetched_at": "2026-08-28T04:05:37.164468+00:00", "kind": "readme", "missing": false, "url": "https://github.com/jkjung-avt/tensorrt_demos"}, {"content_hash": "4701350547bfcce64119e646206ebcf1605f54dc99683309d7e2eb38c9664a1d", "fetched_at": "2026-08-29T11:01:58.312334+00:00", "kind": "homepage", "missing": false, "url": "https://jkjung-avt.github.io/"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T03:22:58.411314+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "5f34ced225326a1a352be1d35486f6551ebcc6f0dc3cc032932e8cdd2eb221be", "fetched_at": "2026-08-28T04:05:37.164468+00:00", "kind": "readme", "missing": false, "url": "https://github.com/jkjung-avt/tensorrt_demos"}, {"content_hash": "4701350547bfcce64119e646206ebcf1605f54dc99683309d7e2eb38c9664a1d", "fetched_at": "2026-08-29T11:01:58.312334+00:00", "kind": "homepage", "missing": false, "url": "https://jkjung-avt.github.io/"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 39, "longevity": 100, "rhythm": 35}, "computed_at": "2026-09-03T02:20:16.233290+00:00", "flags": ["no_releases"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 2663, "days_push": 366, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 50, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}