July 13, 2026 Dataset Open Access

Radoynova, Martina; Pantze, Samuel; De, Trina; Günther, Ulrik; Yakimovich, Artur

Dublin Core Export

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Radoynova, Martina</dc:creator>
  <dc:creator>Pantze, Samuel</dc:creator>
  <dc:creator>De, Trina</dc:creator>
  <dc:creator>Günther, Ulrik</dc:creator>
  <dc:creator>Yakimovich, Artur</dc:creator>
  <dc:date>2026-07-13</dc:date>
  <dc:description>This repository contains the datasets, 3D rendering scene files, and trained model weights accompanying the manuscript "Metric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AI." The deposited files provide the complete procedural pipeline and resulting artefacts used to evaluate zero-shot instance segmentation of virological plaque assays (based on the VACVPlaque dataset).

Repository Contents


	
	datasets/ Contains the synthetic image datasets generated at three varying levels of perceived realism (High, Medium, Low) and in two sizes (Small [S]: 100 images; Large [L]: 1,000 images). It also includes the Mix dataset, which consists of 90 high-realism synthetic images and 10 real images.
	
	
	blender/ Includes the procedural 3D scene files used to generate the synthetic datasets via Blender. These files can be used manually or coupled with the SynthClaw agentic skill for automated rendering.

	
		
		Scene_High.blend
		
		
		Scene_Medium.blend
		
		
		Scene_Low.blend
		
	
	
	
	model_weights/ Contains the trained single-shot architecture model weights and threshold configurations used to evaluate the zero-shot performance of each generated dataset.

	
		
		Evaluated Conditions: High_L, High_S, Medium_L, Medium_S, Low_L, Low_S, and Mix.
		
		
		Files per Condition: Each subdirectory includes the best and last model weights (weights_best.h5, weights_last.h5) alongside the corresponding threshold optimization parameters (thresholds1.json, thresholds2.json) for large and small object segmentation.
		
	
	
</dc:description>
  <dc:identifier>https://rodare.hzdr.de/record/4780</dc:identifier>
  <dc:identifier>10.14278/rodare.4780</dc:identifier>
  <dc:identifier>oai:rodare.hzdr.de:4780</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-43634</dc:relation>
  <dc:relation>doi:10.14278/rodare.4779</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/health</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/rodare</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>deep learning</dc:subject>
  <dc:subject>synthetic data</dc:subject>
  <dc:subject>procedural image rendering</dc:subject>
  <dc:subject>Blender</dc:subject>
  <dc:subject>computer vision</dc:subject>
  <dc:subject>instance segmentation</dc:subject>
  <dc:title>Metric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AI</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>