cogsys-tuebingen/deephs_fruit
Measuring the ripeness of fruit with Hyperspectral Imaging and Deep Learning
Implements the HS-CNN architecture and HyveConv++ models trained on multi-camera hyperspectral datasets (Specim FX 10, INNO-SPEC Redeye, Corning microHSI) across five fruit types with destructive ripeness labels (firmness, sugar content). Built on PyTorch-Lightning with an optimized training pipeline, it supports multi-fruit classification and regression tasks on raw hyperspectral cube data with configurable model selection via command-line arguments.
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Python
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Jan 09, 2024
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