DOI: 10.5061/dryad.ksn02v7gx
title: Data from: BubbleID: A deep learning framework for bubble interface dynamics analysis

description: This paper presents BubbleID, a sophisticated deep learning architecture
 designed to comprehensively identify both static and dynamic attributes of
 bubbles within sequences of boiling images. By amalgamating segmentation
 powered by Mask R-CNN with SORT-based tracking techniques, the framework
 is capable of analyzing each bubble's location, dimensions, interface
 shape, and velocity over its lifetime and capturing dynamic events such as
 bubble departure. BubbleID is trained and tested on boiling images across
 diverse heater surfaces and operational settings. This paper also offers a
 comparative analysis of bubble interface dynamics prior to and
 post-critical heat flux conditions.
description: # Data from: BubbleID: A deep learning framework for bubble interface
 dynamics analysis Dataset DOI:
 [10.5061/dryad.ksn02v7gx](10.5061/dryad.ksn02v7gx) ## Description of the
 data and file structure This dataset includes raw bubble images, annotated
 images, and saved model weights for
 [BubbleID](https://github.com/cldunlap73/BubbleID), an open-source package
 for bubble identification, tracking, and analysis, and liquid-vapor
 interface dynamics analysis. The bubble images are collected using a
 high-speed camera (Phantom VEO 710L) during pool boiling tests performed
 using the facility described in [Pandey et al.
 2024.](https://www.sciencedirect.com/science/article/pii/S1359431123018367) The bubble images are manually annotated for the liquid-vapor interfaces using the [labelme](https://anaconda.org/conda-forge/labelme) package.  ### Files and variables #### File: ned3-001\_BubbleID.zip **Description:** The zip file includes four folders, i.e., AnnotatedData, Models, TestData, and Tutorials.  The *AnnotatedData* folder includes two subfolders and a readme file. This dataset contains pool boiling image data and annotations made in labelme. * Classes-1: Annotations for liquid-vapor interfaces, including the training set, testing set, and a set from one pool boiling experiment, which does not appear in the other folders (Unseen-Test). - Classes-2: Annotations for bubbles distinguishing between attached and detached bubbles. The *Models* folder includes PyTorch model checkpoint file (.pth) saving the weights of preitrained models, including * seg_model.pth: pre-trained instance segmentation model * class_model.pth: pre-trained classification model The *TestData* includes two subfolders:  * SteadyStateTest for image sequences sampled at 3000 fps.  * TransientSmallTst for image sequences sampled at 150 fps.  The *tutorial* folder includes a Python Notebook script illustrating the steps for using the pretrained model and the BubbleID class for saving and displaying data extracted from bubble images/ videos.  ## Code/software * The images (.jpg) and annotations (.json) can be viewed using the [labelme](https://anaconda.org/conda-forge/labelme "https://anaconda.org/conda-forge/labelme") package.  Yes (and it should be a simple as installing LabelMe and pointing to the directory of the images and JSON files. * The tutorial (.ipynb) can be opened using any Python Notebook packages, e.g., Jupyter Notebook, Google Colab. It can be viewed in Google Colab, but I don't think there is a way (at least not a simple one) to run it in Colab. It can be viewed in Jupyter Notebook and run there if installed correctly * The [PyTorch](https://pytorch.org/ "https://pytorch.org/") package can import the checkpoint file (.pth) in a Python script. This can be done by specifying the path to the weights using the BubbleID class, e.g., *test120=BubbleID.DataAnalysis(imagesfolder,videopath,savefolder,extension,modelweights,"cpu")*