PorkBellyHSI

This repository contains source code showing the model architecture, loss function, and training pipeline used by Engstrøm et al. [1] to generate chemical maps of pork bellies with a modified U-Net [2]. Executing train_unet_chemmap.py will train, validate, and evaluate the modified U-Net under the five-fold cross-validation scheme explained by Engstrøm et al. [1]. Note that this script is only for documentation purposes, as actual training requires access to the dataset by Albano-Gaglio et al. [3].

If you want a U-Net implementation, this repository releases a U-Net implementation under the permissive Apache 2.0 License.

The weights for the ensemble of five modified U-Nets used by Engstrøm et al. [1] is available on Hugging Face.

References

1. O.-C. G. Engstrøm, M. Albano-Gaglio, E. S. Dreier, Y. Bouzembrak, M. Font-i-Furnols, P. Mishra, and K. S. Pedersen (2025). Transforming Hyperspectral Images Into Chemical Maps: An End-to-End Deep Learning Approach

2. O. Ronneberger, P. Fischer, and Thomas Brox (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. MICCAI 2015.

3. M. Albano-Gaglio, P. Mishra, S. W. Erasmus, J. F. Tejeda, A. Brun, B. Marcos, C. Zomeño, and M. Font-i-Furnols (2025). Visible and near-infrared spectral imaging combined with robust regression for predicting firmness, fatness, and compositional properties of fresh pork bellies Meat Science.

Funding

This work has been carried out as part of an industrial Ph.D. project receiving funding from FOSS Analytical A/S and The Innovation Fund Denmark. Grant number 1044-00108B.