Datasets

Converts Roboflow COCO annotations into VOC-style segmentation masks and builds the original SAM 2 dataset layout for the additive manufacturing datasets. It also embeds authorship and capture metadata into the released AMVOS (Additive Manufacturing Video Object Segmentation) dataset. Part of the ORNLxUTK DomainSpecific project.

AMVOS is publicly available on Harvard Dataverse and described in the Data in Brief article.

Datasets

Dataset	Process	Labeled classes (white / green)
TIG	Tungsten inert gas wire arc additive manufacturing (TIG-WAAM)	Feed Wire / Melt Pool
PAW (PLASMA)	Plasma arc welding	Feed Wire / Melt Pool
LHW-DED (MAZAK)	Laser hot-wire directed energy deposition	Feed Wire / Melt Pool
visPOLYMER	Polymer pellet extrusion, visible imaging	Nozzle / Material
irPOLYMER	Polymer pellet extrusion, infrared imaging	Nozzle / Material

Setup

uv sync

Scripts

Script	Purpose
roboflow_to_annotationimage.py	Convert Roboflow _annotations.coco.json polygons into 3-channel PNG masks. White (255, 255, 255) = wire/nozzle; green (0, 255, 0) = melt pool/material
createoriginaldirectorystructure.py	Pair the original frames with their masks and build SAM2images/<dataset>/{JPEGImages,Annotations}, renaming frames to 00000.jpg, 00001.jpg, ...
tag_dataset_metadata.py	Embed authors, location, process, camera, and class metadata into every AMVOS image (EXIF for JPEG, text chunks for PNG)

Usage

# 1. COCO polygons → PNG masks (one subdirectory per dataset, each with train/_annotations.coco.json)
python roboflow_to_annotationimage.py roboflowdata/

# 2. Build the SAM 2 directory layout
#    Source frame directories are set in main()
python createoriginaldirectorystructure.py

# 3. Tag AMVOS images with metadata (modifies files in place)
python tag_dataset_metadata.py --root AMVOS --dry-run
python tag_dataset_metadata.py --root AMVOS

Output Layout

SAM2images/<dataset>/
├── JPEGImages/{train,test}/<video_id>/00000.jpg, ...
└── Annotations/{train,test}/<video_id>/00000.png, ...

Citation

If you use this code or dataset in your research, please cite:

@article{wetzel2026domainspecific,
  title={Domain-specific adaptation: low-rank adaptation fine-tuning of {SAM} 2 for manufacturing processes},
  author={Wetzel, C. and Haley, J. and Paquit, V. and Orlyanchik, V. and Santos-Villalobos, H.},
  journal={Journal of Intelligent Manufacturing},
  year={2026},
  doi={10.1007/s10845-026-02973-6}
}

@article{wetzel2026amvosdib,
  title={Cross domain additive manufacturing video object segmentation dataset},
  author={Wetzel, Calvin and Santos-Villalobos, Hector and Haley, James and Orlyanchik, Vladimir and Rodriguez Parra, Mario and Paramanathan, Mithulan and Feldhausen, Tom and Sebok, Michael and Masuo, Chris and Paquit, Vincent},
  journal={Data in Brief},
  pages={113249},
  year={2026},
  doi={10.1016/j.dib.2026.113249}
}

@misc{wetzel2026amvosdataset,
  title={{AMVOS}: Additive Manufacturing Video Object Segmentation Dataset},
  author={Wetzel, Calvin and Santos-Villalobos, Hector and Haley, James and Orlyanchik, Vladimir and Rodriguez Parra, Mario Alberto and Paramanathan, Mithulan and Feldhausen, Thomas and Sebok, Michael and Masuo, Christopher and Paquit, Vincent},
  publisher={Harvard Dataverse},
  version={V1},
  year={2026},
  doi={10.7910/DVN/5GSQTS}
}

Article

[1] C. Wetzel, J. Haley, V. Paquit, V. Orlyanchik, H. Santos-Villalobos, Domain-specific adaptation: low-rank adaptation fine-tuning of SAM 2 for manufacturing processes, Journal of Intelligent Manufacturing (2026). https://doi.org/10.1007/s10845-026-02973-6

[2] C. Wetzel, H. Santos-Villalobos, J. Haley, V. Orlyanchik, M. Rodriguez Parra, M. Paramanathan, T. Feldhausen, M. Sebok, C. Masuo, V. Paquit, Cross domain additive manufacturing video object segmentation dataset, Data in Brief (2026) 113249. https://doi.org/10.1016/j.dib.2026.113249

[3] C. Wetzel, H. Santos-Villalobos, J. Haley, V. Orlyanchik, M.A. Rodriguez Parra, M. Paramanathan, T. Feldhausen, M. Sebok, C. Masuo, V. Paquit, AMVOS: Additive Manufacturing Video Object Segmentation Dataset, Harvard Dataverse, V1 (2026). https://doi.org/10.7910/DVN/5GSQTS