5,266,410 AI resources indexedWorking toward the world’s largest AI resource index.

Browse resources 12 sections
PDF

BeetleFlow: An Integrative Deep Learning Pipeline for Beetle Image Processing

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.

By Liu, Fangxun, Rayeed, S M, Stevens, Samuel, East, Alyson, Chiang, Cheng Hsuan, Lee, Colin, Yi, Daniel, Yang, Junke, Naik, Tejas, Wang, Ziyi, Kilrain, Connor, Buckwalter, Elijah H., Hou, Jiacheng, Bueno, Saul Ibaven, Wang, Shuheng, Ma, Xinyue, Liu, Yifan, Tao, Zhiyuan, Zhang, Ziheng, Sokol, Eric, Belitz, Michael, Record, Sydne, Stewart, Charles V., Chao, Wei-LunToolReference information

Resource information

Format
zip
Original repository
21251294
DOI
10.5281/zenodo.21251294
Credit basis
Creators in Zenodo record metadata
Access
Open files available from the original Zenodo deposit
Last checked
Nov 5, 2026
License
mit-license
Last record update
Nov 5, 2026

About this resource

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.