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get_pose_net

Syntologyentry name in harvested coderead from the graph 2026-09-24

get_pose_net appears in the code Syntology harvested for 15 papers, as 22 distinct code bodies found in 31 places (a place is one code body under one paper). At least one of them ran in 0 of the papers; 0 of the code bodies carry a behaviour fingerprint.

What this page is not. Routines are grouped here by the exact string of their function or class name. Nothing asserts that two samples named get_pose_net do the same thing, share code, or are comparable; the name is a string, not an identity. Behaviour outputs (what a fingerprinted sample returned on the shared battery) are not in this export and are not shown here; the graph at syntology.ai holds them. "Ran" means executed on a synthesized fixture, not that the code is correct or reproduces a paper.

Samples Syntology

Syntology ran 0 of the 22 distinct code bodies named get_pose_net; 22 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
0ran · our draft was wrong
0ran · fixture could not drive it
0ran
22unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 5 of the 31 places are pointer only (Syntology does not serve that copy's text). This site shows no code text for any sample; every row below links to the file in its repository where the record names one.

“Ran” means the sample executed on a synthesized input; it does not mean the output is correct. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code, and those samples did run. The ran count above is every status except unverified, the same rule as each paper page.

Papers

15 papers shown of 15, newest first; 31 places in the table. A paper with no recorded date is placed by the month its arXiv id encodes, shown in the Date column as YYYY-MM (from id). One row per place: a paper whose repository defines the name more than once appears more than once, and the same code body held for several papers appears once under each, with the same status. Titles and dates are the archive's archive 2025-07-28 for papers in the archive, and the graph's for 1 papers added by Syntology. Status and fingerprint are Syntology's record of each code body; licence is recorded for each place. The File cell ends with the code body's code_sha256, Syntology's identity for that exact code: an agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

PaperDateFileStatus SyntologyLicence
Human-in-the-loop adaptation in group activity feature learning for team sports video retrieval added by Syntology 2026-02 (from id) chihina/GAFL-FINE-CVIU/sub_model.py 174384a077e91e20 unverified Apache-2.0 (permissive)
DECO: Dense Estimation of 3D Human-Scene Contact In The Wild 26 Sep 2023 sha2nkt/deco/models/deco.py 8c72b232bd94f8d4 unverified licence not identified · pointer only
HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution Estimation 11 May 2023 akashsengupta1997/humaniflow/models/pose2D_hrnet.py 2534c81208f21998 unverified MIT (permissive)
Scene-aware Egocentric 3D Human Pose Estimation 20 Dec 2022 jianwang-mpi/SceneEgo/network/pose_resnet.py f7ea42152a342da0 unverified no licence file found · pointer only
When Human Pose Estimation Meets Robustness: Adversarial Algorithms and Benchmarks 13 May 2021 leoxiaobin/deep-high-resolution-net.pytorch/lib/models/pose_hrnet.py 2534c81208f21998 unverified MIT (permissive)
When Human Pose Estimation Meets Robustness: Adversarial Algorithms and Benchmarks 13 May 2021 HRNet/HigherHRNet-Human-Pose-Estimation/lib/models/pose_higher_hrnet.py b1587fcc78f6fa72 unverified MIT (permissive)
3D Bird Reconstruction: a Dataset, Model, and Shape Recovery from a Single View 13 Aug 2020 marcbadger/avian-mesh/keypoint_detection/pose_hrnet.py c58c94eb8ab132e0 unverified MIT (permissive)
RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving 10 Jan 2020 maudzung/RTM3D/src/models/fpn_resnet.py 53da3f0e2c240363 unverified MIT (permissive)
RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving 10 Jan 2020 maudzung/RTM3D/src/models/resnet.py da3cdf1c124aa18f unverified MIT (permissive)
Distribution-Aware Coordinate Representation for Human Pose Estimation 14 Oct 2019 Klawens/SwinPose/lib/models/pose_hrnet.py 78015ae361e585ca unverified Apache-2.0 (permissive)
Distribution-Aware Coordinate Representation for Human Pose Estimation 14 Oct 2019 Klawens/SwinPose/lib/models/hourglass.py 498dab54632ae89c unverified Apache-2.0 (permissive)
Distribution-Aware Coordinate Representation for Human Pose Estimation 14 Oct 2019 Klawens/SwinPose/lib/models/pose_resnet.py dda50630404ba452 unverified Apache-2.0 (permissive)
Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement 30 Aug 2019 BoChenYS/satellite-pose-estimation/landmark_regression/lib/models/my_hrnet768.py dcd47884bf1d798d unverified MIT (permissive)
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation 27 Aug 2019 anshky/HR-NET/lib/models/pose_hrnet.py 2534c81208f21998 unverified MIT (permissive)
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation 27 Aug 2019 abhi1kumar/hrnet_pose_single_gpu/lib/models/pose_hrnet.py 78015ae361e585ca unverified MIT (permissive)
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation 27 Aug 2019 LiuShenLan/HRNet/lib/models/pose_higher_hrnet.py b1587fcc78f6fa72 unverified MIT (permissive)
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation 27 Aug 2019 abhi1kumar/hrnet_pose_single_gpu/lib/models/pose_resnet.py 179c4caeed1a0d16 unverified MIT (permissive)
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation 27 Aug 2019 ducongju/HRNet/lib/models/pose_hrnet.py 92f669c4ac6961d1 unverified MIT (permissive)
Deep High-Resolution Representation Learning for Visual Recognition 20 Aug 2019 identical code first harvested elsewhere 2534c81208f21998 unverified licence of this copy not recorded
Learnable Triangulation of Human Pose 14 May 2019 karfly/learnable-triangulation-pytorch/mvn/models/pose_resnet.py 4b5bb331e9336255 unverified MIT (permissive)
Objects as Points 16 Apr 2019 Ssong24/CenterNet_Custom/src/lib/models/networks/resnet_dcn.py 6b6d92280fd55255 unverified MIT (permissive)
Objects as Points 16 Apr 2019 PingoLH/CenterNet-HarDNet/src/lib/models/networks/hardnet.py 52324b791723fee9 unverified MIT (permissive)
Objects as Points 16 Apr 2019 dheerajpreddy/CenterNet-without-DCN/src/lib/models/networks/resnet_dcn.py ae039cec12ddf2f5 unverified MIT (permissive)
Objects as Points 16 Apr 2019 lee-man/movenet/src/lib/models/networks/movenet.py 336a62eb13aca943 unverified MIT (permissive)
Objects as Points 16 Apr 2019 maudzung/SFA3D/sfa/models/fpn_resnet.py cc17d49a7b09fc05 unverified MIT (permissive)
Objects as Points 16 Apr 2019 maudzung/SFA3D/sfa/models/resnet.py 6fa872593fc6762b unverified MIT (permissive)
High-Resolution Representations for Labeling Pixels and Regions 9 Apr 2019 identical code first harvested elsewhere 2534c81208f21998 unverified licence of this copy not recorded
High-Resolution Representations for Labeling Pixels and Regions 9 Apr 2019 identical code first harvested elsewhere 78015ae361e585ca unverified licence of this copy not recorded
Deep High-Resolution Representation Learning for Human Pose Estimation 25 Feb 2019 wsjzha/deep-high-resolution-net.pytorch/lib/models/pose_hrnet.py 2534c81208f21998 unverified MIT (permissive)
Deep High-Resolution Representation Learning for Human Pose Estimation 25 Feb 2019 visionNoob/hrnet_pytorch/lib/models/pose_hrnet.py 78015ae361e585ca unverified MIT (permissive)
Deep High-Resolution Representation Learning for Human Pose Estimation 25 Feb 2019 Microsoft/human-pose-estimation.pytorch/lib/models/pose_resnet.py 9dfb17266423d20d unverified MIT (permissive)

This site shows no code text; each File cell links to the file on GitHub at the repository's current default branch, which may have changed since the harvest. "Pointer only" means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence cell for the reason. Per-sample records for a paper are on its paper page under "Code Syntology ran".

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