Update README.md

README.md

@@ -48,6 +48,9 @@ The model we are finetuning, microsoft/swin-large-patch4-window12-384-in22k, was
 Data augmentation was applied to the training data in a custom Torch dataset class. Because of the size of the dataset, images were not replaced but were duplicated and augmented.
 The only augmentations applied were HorizontalFlips and Rotations (10 degrees) to align with the relatively homogenous dataset.
 
+# Finetuning
+Finetuning was done on some 50 different models including different VTs and CNNs. All models were trained for 10 epochs with the best model, based on the evaluation acccuracy,
+saved every epoch.
 ### Finetuning Data
 
 The finetuning data is a subset  of the cub-200-2011 dataset, https://paperswithcode.com/dataset/cub-200-2011.
@@ -77,8 +80,9 @@ We evaluated the model on 393 samples of the labeled dataset we were given, stra
 
 #### Testing Data
 
-The testing data is a subset of an unlabeled subset of the cub-200-2011 dataset, https://paperswithcode.com/dataset/cub-200-2011 of 4000 images. 
-The predictions of the unlabeled test set were submitted to the Kaggle competiion via CSV which returned the test accuracy.
+The testing data is a subset of an unlabeled subset of the cub-200-2011 dataset, https://paperswithcode.com/dataset/cub-200-2011 of 4000 images. After model finetuning
+the best model, based on the evaluation data would be loaded. This model would be used to predict the labels of the unlabeled test set.
+These predicted labels were submitted to the Kaggle competiion via CSV which returned the test accuracy.
 
 ### Poster