Update README.md

README.md

@@ -43,7 +43,7 @@ The training dataset consists of 1216 labels corresponding to CT scans derived f
 Detailed information on the labeling process can be found [here](https://huggingface.co/datasets/fhofmann/VertebralBodiesCT-Labels).
 The model segments thoracic vertebrae T1 to T12 (labels 1-12), lumbar vertebrae L1 to L5 (labels 13-17), and the sacrum (label 19), with optional vertebrae such as T13 (label 21) or L6 (label 18).
 Training was conducted using 5 folds over 1000 epochs.
-The [dataset_fingerprint.json](nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/dataset_fingerprint.json), [model plan with model configuration and hyperparameters](nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/plans.json), the [splits](splits_final.json) used for 5-fold cross-validation, and training logs of each fold (e.g. [fold 0](https://huggingface.co/fhofmann/VertebralBodiesCT-ResEncL/tree/main/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs) are available with the model.
+The [dataset_fingerprint.json](nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/dataset_fingerprint.json), [model plan with model configuration and hyperparameters](nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/plans.json), the [splits](splits_final.json) used for 5-fold cross-validation, and training logs of each fold (e.g. [fold 0](https://huggingface.co/fhofmann/VertebralBodiesCT-ResEncL/tree/main/nnUNetTrainer__nnUNetResEncUNetLPlans__3d_fullres/fold_0/logs)) are available with the model.
 
 ## Evaluation
 The model's performance metrics were assessed in a separate test dataset, and its clinical value in identifying the third lumbar vertebra (L3) was explored in an [independent oncological dataset](https://doi.org/10.25737/SZ96-ZG60). Model metrics are available [here](metrics.md), and detailed results are described in [our paper](#Citation).