An adapted Cellpose-SAM pipeline achieves 1.50% MAPE on ASTM grain size number G using only two training images while maintaining topological separation better than U-Net, MatSAM, or Qwen2.5-VL-7B.
Lvlm-count: Enhancing the count- ing ability of large vision-language models
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Bridging Foundation Models and ASTM Metallurgical Standards for Automated Grain Size Estimation from Microscopy Images
An adapted Cellpose-SAM pipeline achieves 1.50% MAPE on ASTM grain size number G using only two training images while maintaining topological separation better than U-Net, MatSAM, or Qwen2.5-VL-7B.