Dataset Open Access
Metric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AI
Radoynova, Martina;
Pantze, Samuel;
De, Trina;
Günther, Ulrik;
Yakimovich, Artur
DCAT Export
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<dct:title>Metric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AI</dct:title>
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<dcat:keyword>deep learning</dcat:keyword>
<dcat:keyword>synthetic data</dcat:keyword>
<dcat:keyword>procedural image rendering</dcat:keyword>
<dcat:keyword>Blender</dcat:keyword>
<dcat:keyword>computer vision</dcat:keyword>
<dcat:keyword>instance segmentation</dcat:keyword>
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<dct:description><p>This repository contains the datasets, 3D rendering scene files, and trained model weights accompanying the manuscript <strong>&quot;Metric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AI.&quot;</strong> 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 <a href="https://rodare.hzdr.de/record/3003">VACVPlaque dataset</a>).</p> <p>Repository Contents</p> <ul> <li> <p><strong><code>datasets/</code></strong> 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 <code>Mix</code> dataset, which consists of 90 high-realism synthetic images and 10 real images.</p> </li> <li> <p><strong><code>blender/</code></strong> 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.</p> <ul> <li> <p><code>Scene_High.blend</code></p> </li> <li> <p><code>Scene_Medium.blend</code></p> </li> <li> <p><code>Scene_Low.blend</code></p> </li> </ul> </li> <li> <p><strong><code>model_weights/</code></strong> Contains the trained single-shot architecture model weights and threshold configurations used to evaluate the zero-shot performance of each generated dataset.</p> <ul> <li> <p><strong>Evaluated Conditions:</strong> <code>High_L</code>, <code>High_S</code>, <code>Medium_L</code>, <code>Medium_S</code>, <code>Low_L</code>, <code>Low_S</code>, and <code>Mix</code>.</p> </li> <li> <p><strong>Files per Condition:</strong> Each subdirectory includes the best and last model weights (<code>weights_best.h5</code>, <code>weights_last.h5</code>) alongside the corresponding threshold optimization parameters (<code>thresholds1.json</code>, <code>thresholds2.json</code>) for large and small object segmentation.</p> </li> </ul> </li> </ul></dct:description>
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- Publication date:
- July 13, 2026
- DOI:
-
- Keyword(s):
- deep learning synthetic data procedural image rendering Blender computer vision instance segmentation
- Related identifiers:
- Identical to:
https://www.hzdr.de/publications/Publ-43634 - Communities:
- License (for files):
- Creative Commons Attribution 4.0 International