chore: Provide better train/val split for Udacity dataset

The notebook now provides a new link to the Udacity dataset that contains a better training and validation split than before. Before, the validation dataset contained mainly cars and no byciclists at all, while the new split is more balanced.

Author: pierluigiferrari

ssd7_training.ipynb

@@ -213,7 +213,7 @@
    "source": [
     "### Note:\n",
     "\n",
-    "The example setup below was used to train SSD7 on two road traffic datasets released by [Udacity](https://github.com/udacity/self-driving-car/tree/master/annotations) with around 20,000 images in total and 5 object classes (car, truck, pedestrian, bicyclist, traffic light), although the vast majority of the objects are cars. The original datasets have a constant image size of 1200x1920 RGB. I consolidated the two datasets, removed a few bad samples (although there are probably many more), and resized the images to 300x480 RGB, i.e. to one sixteenth of the original image size. In case you'd like to train a model on the same dataset, you can download the consolidated and resized dataset I used [here](https://drive.google.com/open?id=1uOqIUiJlDwoeL8vnNMacNbkDpDe1eRp-) (about 900 MB)."
+    "The example setup below was used to train SSD7 on two road traffic datasets released by [Udacity](https://github.com/udacity/self-driving-car/tree/master/annotations) with around 20,000 images in total and 5 object classes (car, truck, pedestrian, bicyclist, traffic light), although the vast majority of the objects are cars. The original datasets have a constant image size of 1200x1920 RGB. I consolidated the two datasets, removed a few bad samples (although there are probably many more), and resized the images to 300x480 RGB, i.e. to one sixteenth of the original image size. In case you'd like to train a model on the same dataset, you can download the consolidated and resized dataset I used [here](https://drive.google.com/open?id=1tfBFavijh4UTG4cGqIKwhcklLXUDuY0D) (about 900 MB)."
    ]
   },
   {