Update README.md

README.md

@@ -53,7 +53,7 @@ Finetuning was done on some 50 different models including different VTs and CNNs
 saved every epoch.
 ### Finetuning Data
 
-The finetuning data is a subset  of the cub-200-2011 dataset, https://paperswithcode.com/dataset/cub-200-2011.
+The finetuning data is a subset  of the cub-200-2011 dataset, http://www.vision.caltech.edu/datasets/cub_200_2011/.
 We finetuned the model on 3533 samples of the labeled dataset we were given, stratified on the label (7066 including augmented images).
 
 
@@ -75,14 +75,14 @@ We finetuned the model on 3533 samples of the labeled dataset we were given, str
 *parameters were intentionally left out because of poor results
 
 ### Evaluation Data
-The evaluation data is a subset of the cub-200-2011 dataset, https://paperswithcode.com/dataset/cub-200-2011
+The evaluation data is a subset of the cub-200-2011 dataset, http://www.vision.caltech.edu/datasets/cub_200_2011/.
 We evaluated the model on 393 samples of the labeled dataset we were given, stratified on the label.
 
 #### 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. 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.
+The testing data is a subset of an unlabeled subset of the cub-200-2011 dataset, http://www.vision.caltech.edu/datasets/cub_200_2011/ of 4000 images. After model finetuning
+the best model, based on the evaluation data, would be loaded. This model would then be used to predict the labels of the unlabeled test set.
+These predicted labels were submitted to the Kaggle competition via CSV which returned the test accuracy.
 
 ### Poster