Updated link to model_weights/

README.md

@@ -1,7 +1,8 @@
 # PorkBellyHSI
 ![](graphical_abstract.png)
 
-This repository contains source code showing the model architecture, loss function, and training pipeline used by Engstrøm et al. [[1]](#references) to generate chemical maps of pork bellies with a modified U-Net [[2]](#references). Executing [train_unet_chemmap.py](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]](#references). Likewise, [load_ensemble_unet.py](load_ensemble_unet.py) loads an ensemble of the five U-Nets (weights stored in [model_weights/](model_weights/)) and uses them to make the predictions shown in [ensemble_prediction.png](ensemble_prediction.png) and [ensemble_prediction_masked.png](ensemble_prediction_masked.png).
+This repository contains source code showing the model architecture, loss function, and training pipeline used by Engstrøm et al. [[1]](#references) to generate chemical maps of pork bellies with a modified U-Net [[2]](#references). Executing [train_unet_chemmap.py](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]](#references). Likewise, [load_ensemble_unet.py](load_ensemble_unet.py) loads an ensemble of the five U-Nets (weights stored in [model_weights/](https://huggingface.co/Sm00thix/unet_chemical_map/tree/main/model_weights/
+)) and uses them to make the predictions shown in [ensemble_prediction.png](ensemble_prediction.png) and [ensemble_prediction_masked.png](ensemble_prediction_masked.png).
 
 Note that these scripts are for documentation purposes as actual training and evaluation require access to the dataset by Albano-Gaglio et al. [[3]](#references).