UARK-NED3/BubbleID-Flow
BubbleID-Flow Multimodal Flow-Boiling Dataset DOI: 10.5281/zenodo.22235802 License: Creative Commons Attribution 4.0 International (CC BY 4.0) Model: the fine-tuned checkpoint is also published on its own at UARK-NED3/BubbleID-Flow, and remains included here under ModelWeights/ so this mirror stays complete against manifest.csv. Mirror: this repository mirrors the Zenodo deposit of 1 September 2026. The file set was verified identical at upload time: 4,456 files, 24 GB. Every… See the full description on the dataset page: https://huggingface.co/datasets/UARK-NED3/BubbleID-Flow.
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1# Processing Snapshot2 3This directory contains a source snapshot of the scripts used for the released4BubbleID-Flow data products. It accompanies, but does not replace, the5canonical software repository:6 7https://github.com/UARK-NED3/BubbleID-Flow8 9The repository baseline at the time of this release snapshot was commit10`051c07dd8016cc27861509ac203b59b45b29e170`. The files under `scripts/` are the11archived processing snapshot for this dataset release. Run them from this12directory with `python -m scripts.<module_name>` so intra-script imports resolve13from the snapshot.14 15## Environment16 17`environment.yml` records the environment versions observed during release18preparation: Python 3.10.19, PyTorch 2.9.1 CPU, Detectron2 0.6, OpenCV 4.11.0,19NumPy 1.23.0, and pandas 2.3.3. The environment file is a dependency20specification, not a platform-independent binary lockfile.21 22## Main entry points23 24- `labelme_to_coco.py`: convert LabelMe annotations to ROI-aware COCO format.25- `train_detectron2.py`: fine-tune the one-class Mask R-CNN model.26- `evaluate_detectron2.py`: calculate COCO and union-mask metrics.27- `analyze_segmentation_robustness.py`: reproduce score-threshold and28 segmentation-robustness analyses.29- `audit_thermal_reduction.py`: calculate traceable thermal state summaries.30- `analyze_optical_thermal_association.py`: perform state-level31 optical-thermal screening.32- `analyze_multimodal_case.py`: prepare per-case optical, thermal, and AE33 summaries subject to the timing limits in `../timing_reconciliation.csv`.34- `create_bubbleid_flow_showcase_video.py`: regenerate the derived presentation35 videos under `../Videos`.36 37## Reproduction boundary38 39The release contains the 130 LabelMe files and checkpoint but does not include40a frozen file-level 104/26 train/holdout split manifest, original training41seed, augmentation record, or checkpoint-selection history. It therefore42supports reanalysis and new split construction, but does not independently43reconstruct the reported same-sequence evaluation exactly.44