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127 lines (127 loc) · 3.89 KB
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{
"creators": [
{
"name": "Liu, Fangxun",
"affiliation": "The Ohio State University",
"orcid": "0009-0008-8508-2776"
},
{
"name": "Rayeed, S M",
"affiliation": "Rensselaer Polytechnic Institute"
},
{
"name": "Stevens, Samuel",
"affiliation": "The Ohio State University"
},
{
"name": "East, Alyson",
"affiliation": "The University of Maine"
},
{
"name": "Chiang, Cheng Hsuan",
"affiliation": "The Ohio State University"
},
{
"name": "Lee, Colin",
"affiliation": "The Ohio State University"
},
{
"name": "Yi, Daniel",
"affiliation": "The Ohio State University"
},
{
"name": "Yang, Junke",
"affiliation": "The Ohio State University"
},
{
"name": "Naik, Tejas",
"affiliation": "The Ohio State University"
},
{
"name": "Wang, Ziyi",
"affiliation": "The Ohio State University"
},
{
"name": "Kilrain, Connor",
"affiliation": "The Ohio State University"
},
{
"name": "Buckwalter, Elijah H.",
"affiliation": "The Ohio State University"
},
{
"name": "Hou, Jiacheng",
"affiliation": "The Ohio State University"
},
{
"name": "Bueno, Saul Ibaven",
"affiliation": "The Ohio State University"
},
{
"name": "Wang, Shuheng",
"affiliation": "The Ohio State University"
},
{
"name": "Ma, Xinyue",
"affiliation": "The Ohio State University"
},
{
"name": "Liu, Yifan",
"affiliation": "The Ohio State University"
},
{
"name": "Tao, Zhiyuan",
"affiliation": "The Ohio State University"
},
{
"name": "Zhang, Ziheng",
"affiliation": "The Ohio State University"
},
{
"name": "Sokol, Eric",
"affiliation": "National Ecological Observatory Network (NEON), Battelle"
},
{
"name": "Belitz, Michael",
"affiliation": "Michigan State University"
},
{
"name": "Record, Sydne",
"affiliation": "The University of Maine"
},
{
"name": "Stewart, Charles V.",
"affiliation": "Rensselaer Polytechnic Institute"
},
{
"name": "Chao, Wei-Lun",
"affiliation": "The Ohio State University"
}
],
"description": "Pipeline for processing large-scale tray images of pinned beetles: detect specimens, optionally sort and crop with metadata matching, and produce fine-grained morphological segmentation (5-class or 9-class). It uses an open-vocabulary detector (Grounding DINO), a vision-language model (LLaVA-NeXT) for verification, and fine-tuned Mask2Former for transformer-based part segmentation and downstream morphological analysis.",
"keywords": [
"deep learning",
"computer vision",
"image processing",
"object detection",
"image segmentation",
"entomology",
"imageomics"
],
"license": {
"id": "MIT"
},
"publication_date": "2026-07-07",
"title": "BeetleFlow: An Integrative Deep Learning Pipeline for Beetle Image Processing",
"version": "1.0.0",
"grants": [
{
"id": "021nxhr62::2118240"
}
],
"references": [
"East, A., Belitz, M., Cotton, L., Dominguez, J., Betancourt, I., Rayeed, S. M., Liu, F., Carlyn, D., Kilrain, C., Wu, J., Earl, C., Lapp, H., Perry, K. I., Stewart, C., Thompson, M. J., Campolongo, E. G., Chao, W.-L., Sokol, E. R., & Record, S. (2026). Beetles as Sentinel Taxa: Predicting drought conditions from NEON specimen imagery (Revision 5026be7). Hugging Face. https://doi.org/10.57967/hf/8716",
"National Ecological Observatory Network. (2025). Ground beetles sampled from pitfall traps (DP1.10022.001). https://doi.org/10.48443/CD21-Q875",
"National Ecological Observatory Network. (2025). NEON Field Site Metadata. Retrieved June 25, 2025, from https://www.neonscience.org/field-sites/exports/NEON_Field_Site_Metadata_20250625"
]
}