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get_model_params

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

get_model_params appears in the code Syntology harvested for 15 papers, as 17 distinct code bodies found in 19 places (a place is one code body under one paper). At least one of them ran in 8 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_model_params 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 7 of the 17 distinct code bodies named get_model_params; 10 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
1ran · our draft was wrong
0ran · fixture could not drive it
6ran
10unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 10 of the 19 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; 19 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 2 papers added by Syntology; 1 papers have no page here and are shown by arXiv id only. 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
DrawMotion : Generating 3D Human Motions by Freehand Drawing added by Syntology 2026-05 (from id) InvertedForest/DrawMotion/my_tools.py 60a085623765d161 ran licence not identified · pointer only
Learning Fine-Grained Correspondence with Cross-Perspective Perception for Open-Vocabulary 6D Object Pose Estimation added by Syntology 2026-01 (from id) zjjqinyu/FiCoP/bop_toolkit_lib/dataset_params.py c72662e2a81ebee8 unverified no licence file found · pointer only
One2Any: One-Reference 6D Pose Estimation for Any Object 7 May 2025 lmy1001/One2Any/bop_toolkit_lib/dataset_params.py c72662e2a81ebee8 unverified MIT (permissive)
Restructuring Vector Quantization with the Rotation Trick 8 Oct 2024 cfifty/rotation_trick/src/models/model_utils.py 13e83475b653975d ran no licence file found · pointer only
BlockPruner: Fine-grained Pruning for Large Language Models 15 Jun 2024 MrGGLS/BlockPruner/block_search.py ab9ed9d781d2c6a7 ran no licence file found · pointer only
Causal Action Influence Aware Counterfactual Data Augmentation 29 May 2024 martius-lab/caiac/causal_slr/model_training/train_model.py 6116b4dbd10c2ae8 ran no licence file found · pointer only
vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention 7 May 2024 microsoft/vattention/microbenchmarks/perf_pagesize/bench_pagesize.py c9f5420b1cecba17 ran MIT (permissive)
Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection 4 Mar 2024 qingyuliu/exposing-the-deception/models/MI_Net.py c58da27c524ad799 unverified Apache-2.0 (permissive)
Seeing the Unseen: Learning Basis Confounder Representations for Robust Traffic Prediction 21 Nov 2023 bigscity/steve_code/STEVE/lib/utils.py 1eb45f17c7df7591 ran MIT (permissive)
Unifying Visual Perception by Dispersible Points Learning 18 Aug 2022 Sense-X/UniHead/up/models/backbones/efficientnet.py 86782e22d067e4c3 unverified Apache-2.0 (permissive)
Model-based 3D Hand Reconstruction via Self-Supervised Learning 22 Mar 2021 TerenceCYJ/S2HAND/efficientnet_pt/model.py d2a7b16829a6a779 unverified no licence file found · pointer only
Multi-attentional Deepfake Detection 3 Mar 2021 yoctta/multiple-attention/models/MAT.py 3872570123f9e3ae unverified no licence file found · pointer only
BOP Challenge 2020 on 6D Object Localization 15 Sep 2020 thodan/bop_toolkit/bop_toolkit_lib/dataset_params.py e3a553daadce72d4 unverified MIT (permissive)
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 28 May 2019 github-luffy/PFLD_68points_Pytorch/efficientnet/model.py 18640da0a6568157 ran · our draft was wrong no licence file found · pointer only
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 28 May 2019 shijianjian/efficientnet-pytorch-3d/efficientnet_pytorch_3d/model.py cec8456f1fb53df1 unverified Apache-2.0 (permissive)
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 28 May 2019 AmirmohammadRostami/KeywordsSpotting-EfficientNet-A0/utils/efficientnet_pytorch/model.py eec6dd2ad88dd583 unverified MIT (permissive)
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 28 May 2019 DableUTeeF/keras-efficientnet/keras_efficientnet/efficientnet_builder.py cfa3ae101a95697f unverified no licence file found · pointer only
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 28 May 2019 ravi02512/efficientdet-keras/backbone/efficientnet_builder.py 92f38ceb21745450 unverified no licence file found · pointer only
arXiv:Wang_SynSP_Synergy_of_Smoothness_and_Precision_in_Pose_Sequences_Refinement_CVPR_2024_paper InvertedForest/SynSP/my_tools.py 60a085623765d161 ran Apache-2.0 (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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