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unique_boxes

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

unique_boxes appears in the code Syntology harvested for 21 papers, as 2 distinct code bodies found in 21 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 unique_boxes 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 2 distinct code bodies named unique_boxes; 2 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
2unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 0 of the 21 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

21 papers shown of 21, newest first; 21 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
Cross-Domain Object Detection Using Unsupervised Image Translation added by Syntology 2026-01 (from id) endernewton/tf-faster-rcnn/lib/datasets/ds_utils.py 8a015c012f507631 unverified MIT (permissive)
Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection 2 Mar 2024 ssbin0914/Causal-Mode-Multiplexer/lib/datasets/ds_utils.py 8a015c012f507631 unverified MIT (permissive)
Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement 6 Jan 2022 dliu5812/ddf/lib/datasets/ds_utils.py 8a015c012f507631 unverified MIT (permissive)
Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection 28 Oct 2021 dongnana777/Bridging-Non-Co-occurrence/lib/datasets/ds_utils.py 8a015c012f507631 unverified MIT (permissive)
Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection 17 Sep 2020 GanlongZhao/CST_DA_detection/lib/datasets/ds_utils.py 8a015c012f507631 unverified MIT (permissive)
Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild 23 Jul 2020 YoungXIAO13/FewShotDetection/lib/datasets/ds_utils.py 8a015c012f507631 unverified MIT (permissive)
Understanding Human Hands in Contact at Internet Scale 11 Jun 2020 ddshan/hand_object_detector/lib/datasets/ds_utils.py 8a015c012f507631 unverified MIT (permissive)
Cross-Domain Document Object Detection: Benchmark Suite and Method 30 Mar 2020 kailigo/cddod/lib/datasets/ds_utils.py 8a015c012f507631 unverified MIT (permissive)
One-Shot Object Detection with Co-Attention and Co-Excitation 28 Nov 2019 timy90022/One-Shot-Object-Detection/lib/datasets/ds_utils.py 8a015c012f507631 unverified MIT (permissive)
Progressive Domain Adaptation for Object Detection 24 Oct 2019 kevinhkhsu/DA_detection/lib/datasets/ds_utils.py 8a015c012f507631 unverified MIT (permissive)
Distilling Object Detectors with Fine-grained Feature Imitation 9 Jun 2019 twangnh/Distilling-Object-Detectors/lib/datasets/ds_utils.py 8a015c012f507631 unverified MIT (permissive)
Differentiable Scene Graphs 26 Feb 2019 shikorab/DSG/lib/datasets/ds_utils.py 8a015c012f507631 unverified MIT (permissive)
Localization Recall Precision (LRP): A New Performance Metric for Object Detection 4 Jul 2018 cancam/LRP/pascal-voc-lrp/utils/ds_utils.py 8a015c012f507631 unverified MIT (permissive)
Learning Rich Features for Image Manipulation Detection 13 May 2018 pengzhou1108/RGB-N/lib/datasets/ds_utils.py 8a015c012f507631 unverified MIT (permissive)
Detect to Track and Track to Detect 11 Oct 2017 Feynman27/pytorch-detect-to-track/lib/datasets/ds_utils.py 8a015c012f507631 unverified MIT (permissive)
Feature Pyramid Networks for Object Detection 9 Dec 2016 adityaarun1/pytorch_fast-er_rcnn/lib/datasets/ds_utils.py 8a015c012f507631 unverified MIT (permissive)
Detecting Text in Natural Image with Connectionist Text Proposal Network 12 Sep 2016 Sanster/tf_ctpn/lib/datasets/ds_utils.py 8a015c012f507631 unverified MIT (permissive)
R-FCN: Object Detection via Region-based Fully Convolutional Networks 20 May 2016 Feynman27/pytorch-detect-rfcn/lib/datasets/ds_utils.py 8a015c012f507631 unverified MIT (permissive)
Weakly Supervised Deep Detection Networks 9 Nov 2015 adursun/wsddn.pytorch/src/utils.py c5f73cca2b8886ae unverified Apache-2.0 (permissive)
Spatial Transformer Networks 5 Jun 2015 chenwuperth/rgz_rcnn/lib/datasets/ds_utils.py 8a015c012f507631 unverified MIT (permissive)
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks 4 Jun 2015 aleksispi/drl-rpn-tf/lib/datasets/ds_utils.py 8a015c012f507631 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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