Update main.py

main.py

@@ -73,24 +73,23 @@ annotation_folder = '/annotations/'
 if not os.path.exists(os.path.abspath('.') + annotation_folder):
   annotation_zip = tf.keras.utils.get_file('captions.zip',
                                            cache_subdir=os.path.abspath('.'),
-                                           origin='http://images.cocodataset.org/annotations/annotations_trainval2017.zip',
+                                           origin='http://images.cocodataset.org/annotations/annotations_trainval2014.zip',
                                            extract=True)
-  annotation_file = os.path.dirname(annotation_zip)+'/annotations/captions_train2017.json'
+  annotation_file = os.path.dirname(annotation_zip)+'/annotations/captions_train2014.json'
   os.remove(annotation_zip)
 
 # Download image files
-image_folder = '/train2017/'
+image_folder = '/train2014/'
 if not os.path.exists(os.path.abspath('.') + image_folder):
-  image_zip = tf.keras.utils.get_file('train2017.zip',
+  image_zip = tf.keras.utils.get_file('train2014.zip',
                                       cache_subdir=os.path.abspath('.'),
-                                      origin='http://images.cocodataset.org/zips/train2017.zip',
+                                      origin='http://images.cocodataset.org/zips/train2014.zip',
                                       extract=True)
   PATH = os.path.dirname(image_zip) + image_folder
   os.remove(image_zip)
 else:
   PATH = os.path.abspath('.') + image_folder
 
-PATH
 
 """## Optional: limit the size of the training set
 To speed up training for this tutorial, you'll use a subset of 30,000 captions and their corresponding images to train your model. Choosing to use more data would result in improved captioning quality.