UARK-NED3/BoilingBench-CV
BoilingBench-CV Dataset Version: v0.1.0 Maintainer: NED3 Laboratory, University of Arkansas License: CC BY 4.0 DOI: 10.5281/zenodo.22264378 Mirror of the Zenodo deposit of 3 September 2026, published here because most users of these data work in the Hugging Face ecosystem. The file set was verified identical to the deposit at upload time: 7,147 files, 4.20 GB uncompressed. Authors Hari Pandey (University of Arkansas), Manohar Bongarala (Purdue University), Christy… See the full description on the dataset page: https://huggingface.co/datasets/UARK-NED3/BoilingBench-CV.
BoilingBench-CV Dataset
Version: v0.1.0 Maintainer: NED3 Laboratory, University of Arkansas License: CC BY 4.0 DOI: 10.5281/zenodo.22264378
Mirror of the Zenodo deposit of 3 September 2026, published here because most users of these data work in the Hugging Face ecosystem. The file set was verified identical to the deposit at upload time: 7,147 files, 4.20 GB uncompressed.
Authors
Hari Pandey (University of Arkansas), Manohar Bongarala (Purdue University), Christy Dunlap (University of Arkansas), Lige Zhang (Drexel University), Justin A. Weibel (Purdue University), Ying Sun (University of North Carolina at Charlotte), and Han Hu (University of Arkansas).
Status and limitations
Read this before reporting results.
- Files inside the package still carry the pre-release version string
0.1.0-internal, andmetadata/data_rights_manifest.csvstill recordsrelease_status = internal package. Both are stale: the package was published as v0.1.0 under CC BY 4.0 with a DOI on 3 September 2026. The files are mirrored here unmodified rather than corrected, so that this copy stays byte-identical to the citable deposit. - The annotation quality-assurance record (`metadata/annotation_qa.md`) lists open follow-up items. 156 contours are flagged: 106 with at least one coordinate outside image bounds and 50 with invalid coordinate counts. Flagged contours are marked
ignore=1and excluded from scoring under the v0.1 screening policy; no manual adjudication has been performed. - The
sha256column ofderived/poolboiling-v0.1/manifest.csvis not populated. Per-file checksums for the canonical annotations and the split files are recorded inderived/poolboiling-v0.1/validation.json. - Some manifest and result files retain absolute paths from the acquisition host. They are provenance records only and are not needed to read the data.
Purpose
Bubble nucleation, growth, coalescence, and departure govern the heat flux a boiling surface can sustain, and measuring those events from high-speed video is the bottleneck in relating surface design to thermal performance. BoilingBench-CV evaluates computer-vision methods for two-dimensional bubble instance segmentation and per-frame bubble morphometry, with explicit attention to whether a method trained in one imaging domain transfers to another.
This package does not establish a tracking or bubble-dynamics benchmark. Per-image contours do not encode reviewed temporal bubble identities, coalescence or breakup events, or departure times.
Contents
Canonical pool-boiling benchmark
derived/poolboiling-v0.1/ covers 357 annotated images carrying 16,106 contour records across four regimes:
Raw subsets
Model checkpoints are excluded from all subsets; checkpoint licenses may differ from image and annotation rights.
Splits and the leakage rule
The independent unit is a source acquisition group, represented by (regime, source_video). Every frame and its derivatives stay in one partition. Four protocols are provided, each spanning 31 groups: pooled_grouped, water_to_hfe, hfe_to_water, and leave_<regime>. validation.json records an empty leaked_groups list for every split.
Note that source_video is a grouping key. The benchmark operates on still images.
Loading
The repository is a file tree rather than a datasets-loadable config, since annotations follow the COCO convention rather than a Hugging Face schema.
The Dataset Viewer shows derived/poolboiling-v0.1/manifest.csv, the 357-row index of the canonical pool-boiling benchmark. It is a browsable index, not the benchmark data: images live under raw/, and the annotations live in derived/poolboiling-v0.1/annotations.json.
from huggingface_hub import snapshot_download
path = snapshot_download(
repo_id="UARK-NED3/BoilingBench-CV",
repo_type="dataset",
)To pull only the canonical benchmark and skip the large source videos:
path = snapshot_download(
repo_id="UARK-NED3/BoilingBench-CV",
repo_type="dataset",
allow_patterns=["derived/*", "splits/*", "metadata/*", "docs/*",
"raw/PoolBoilingDatasets/**/annotatedBubbles/*"],
)Then read derived/poolboiling-v0.1/annotations.json with any COCO tool, for example pycocotools.coco.COCO.
Domain shift, and how not to over-read it
The water and HFE-7100 regimes differ in more than working fluid. Surface structure, image geometry, optical arrangement, resolution, and facility may all differ between them. A water-versus-HFE performance gap is therefore a compound domain effect and must not be interpreted as a fluid-only causal effect. Report performance by regime, not only as a pooled score.
Reference screening result
A frozen-checkpoint screening on the water_to_hfe test partition (175 HFE-7100 frames) is recorded in derived/poolboiling-v0.1/results/MODEL_COMPARISON.md. No benchmark-label training, threshold tuning, or model adaptation was used.
This is an off-the-shelf cross-domain screening result, not a claim of generalized validation. Each checkpoint was applied outside the domain it was fitted for, so these numbers characterize transfer behavior under the stated protocol and should not be read as a ranking of method quality.
Out-of-scope use
Do not use this version to claim validated three-dimensional vapor volume, void fraction, tracking accuracy, bubble lifetime, departure frequency, or coalescence or breakup event accuracy. Those require calibration and temporal identity or event annotations that are not part of this version.
Use rules
- Preserve
raw/unchanged. - Use only the supplied grouped split files for reported benchmark results.
- Do not split frames from the same
(regime, source video/power condition)across partitions. - Report performance by regime, not only a pooled score.
- Treat annotations flagged in
metadata/annotation_qa.mdaccording to the benchmark evaluator's documented ignore policy.
Attribution
Cite the BoilingBench-CV dataset version, the Zenodo DOI 10.5281/zenodo.22264378, and the source records listed in metadata/data_sources.csv.
Upstream sources
The deposit records these relations. Honor them alongside the CC BY 4.0 attribution requirement:
Overlap with PoolBoiling-HighSpeedVideo
The 10.5061/dryad.kh18932mw deposit is mirrored on the Hub as UARK-NED3/PoolBoiling-HighSpeedVideo, and the two repositories share content: all 357 pool-boiling annotated images, all four BubbleContours.json files, and 8 of the PSi-HFE videos here also appear there.
Use this repository for the canonical COCO-style annotations, the grouped benchmark splits and their leakage rule, and the segmentation evaluation records. Use PoolBoiling-HighSpeedVideo for the complete high-speed video set (31 videos across four regimes), the original contour JSON format, and the MATLAB descriptor toolkit. The licenses differ — this package is CC BY 4.0, that deposit is CC0 1.0 — so check the terms for the copy you actually use.
Source method papers:
- C. Dunlap et al., "BubbleID: A Deep Learning Framework for Bubble Interface Dynamics Analysis," Journal of Applied Physics 136, 014902 (2024).
- A. Fahim et al., "BubbleID-Flow: Machine-Vision Quantification of Vapor Area Fraction in Subcooled Flow Boiling," manuscript in preparation.
The CC BY 4.0 license applies to this package as organized here. It does not erase attribution obligations or source-specific terms recorded in metadata/data_sources.csv and metadata/data_rights_manifest.csv.
Start here
What this resource supports. BoilingBench-CV is a pool-boiling benchmark for two-dimensional bubble instance segmentation and per-frame morphometry. Use the released grouped splits and report results by regime; the water/HFE-7100 comparison combines fluid, surface, image geometry, optics, and facility differences, so it is not a fluid-only causal comparison. First five minutes. Download the immutable file tree, then inspect the manifest and canonical annotations:
from huggingface_hub import snapshot_download
root = snapshot_download(
repo_id="UARK-NED3/BoilingBench-CV",
repo_type="dataset",
)
print(root)The Dataset Viewer previews derived/poolboiling-v0.1/manifest.csv. The images are under raw/; canonical annotations are in derived/poolboiling-v0.1/annotations.json. Use with care. This release does not establish tracking accuracy, three-dimensional vapor volume, void fraction, bubble lifetime, departure frequency, or coalescence/breakup-event accuracy. Preserve the raw data and use the supplied grouped benchmark partitions for reported results. Continue. Canonical benchmark repository: https://github.com/UARK-NED3/BoilingBench-CV NED³ datasets catalog: https://ned3.uark.edu/datasets/
