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BatchNorm2d

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

BatchNorm2d appears in the code Syntology harvested for 9 papers, as 8 distinct code bodies found in 10 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 BatchNorm2d 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 8 distinct code bodies named BatchNorm2d; 1 is 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
7ran
1unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 5 of the 10 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

9 papers shown of 9, newest first; 10 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. 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
MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual Grounding 5 Mar 2024 dfki-av/mikasa-3dvg/models/MiKASA_transformer.py c9ea4b32ab8ee01d ran no licence file found · pointer only
UnIVAL: Unified Model for Image, Video, Audio and Language Tasks 30 Jul 2023 mshukor/unival/models/unival/unify_transformer.py f18199d037514305 ran Apache-2.0 (permissive)
Table and Image Generation for Investigating Knowledge of Entities in Pre-trained Vision and Language Models 3 Jun 2023 OFA-Sys/OFA/models/ofa/unify_transformer.py f18199d037514305 ran Apache-2.0 (permissive)
PolyFormer: Referring Image Segmentation as Sequential Polygon Generation 14 Feb 2023 amazon-science/polygon-transformer/models/polyformer/unify_transformer.py f18199d037514305 ran Apache-2.0 recorded; this copy not marked cleared · pointer only
OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds 10 Oct 2022 vlar-group/ogc/models/segnet_ogcdr.py ea90589983c7d4fb ran · metamorphic tier: deterministic licence not identified · pointer only
Pruning neural networks without any data by iteratively conserving synaptic flow 9 Jun 2020 iurada/px-ntk-pruning/lib/pruners.py bd2e9ef9ef047b34 ran no licence file found · pointer only
EfficientDet: Scalable and Efficient Object Detection 20 Nov 2019 SharifElfouly/easy-model-zoo/easy_model_zoo/bisenet/bisenet.py d3697beae5965412 unverified MIT (permissive)
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space 7 Jun 2017 zyang-ur/SAT/referit3d/external_tools/pointnet2/pointnet2_modules.py 1eb36d9951cd4e0a ran · metamorphic tier: deterministic MIT (permissive)
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space 7 Jun 2017 referit3d/referit3d/referit3d/models/backbone/point_net_pp.py f9032ebfafd355b4 ran · metamorphic tier: deterministic MIT (permissive)
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks 9 Mar 2017 fmu2/PyTorch-MAML/models/maml.py 8bdb2b9d394d85bf ran · metamorphic tier: deterministic no licence file found · pointer only

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".

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections