Update renovation.py

renovation.py

@@ -19,17 +19,21 @@ _CITATION = """\
 
 _DESCRIPTION = """\
 Renovations is a dataset of images of houses taken in the field using smartphone
-cameras. It consists of 3 classes: cheap, average, and expensive renovations.
+cameras. It consists of 7 classes: Not Applicable, Very Poor, Poor, Fair, Good, Excellent, and Exceptional renovations.
 Data was collected by the your research lab.
 """
 
 _URLS = {
-    "cheap": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/cheap.zip",
-    "average": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/average.zip",
-    "expensive": "https://huggingface.co/datasets/rshrott/renovation/resolve/main/expensive.zip",
+    "Not Applicable": "https://huggingface.co/datasets/rshrott/photos/resolve/main/Not Applicable.zip",
+    "Very Poor": "https://huggingface.co/datasets/rshrott/photos/resolve/main/Very Poor.zip",
+    "Poor": "https://huggingface.co/datasets/rshrott/photos/resolve/main/Poor.zip",
+    "Fair": "https://huggingface.co/datasets/rshrott/photos/resolve/main/Fair.zip",
+    "Good": "https://huggingface.co/datasets/rshrott/photos/resolve/main/Good.zip",
+    "Excellent": "https://huggingface.co/datasets/rshrott/photos/resolve/main/Excellent.zip",
+    "Exceptional": "https://huggingface.co/datasets/rshrott/photos/resolve/main/Exceptional.zip"
 }
 
-_NAMES = ["cheap", "average", "expensive"]
+_NAMES = ["Not Applicable", "Very Poor", "Poor", "Fair", "Good", "Excellent", "Exceptional"]
 
 class Renovations(datasets.GeneratorBasedBuilder):
     """Renovations house images dataset."""
@@ -75,32 +79,30 @@ class Renovations(datasets.GeneratorBasedBuilder):
                 },
             ),
         ]
+
     def _generate_examples(self, data_files, split):
         all_files_and_labels = []
         for label, path in data_files.items():
             files = glob.glob(path + '/*.jpeg', recursive=True)
             all_files_and_labels.extend((file, label) for file in files)
-    
+
         random.seed(43)  # ensure reproducibility
         random.shuffle(all_files_and_labels)
-    
+
         num_files = len(all_files_and_labels)
         train_data = all_files_and_labels[:int(num_files*0.9)]
-        val_test_data = all_files_and_labels[int(num_files*0.9):] # This will be used for both val and test
-    
+        val_test_data = all_files_and_labels[int(num_files*0.9):]  # This will be used for both val and test
+
         if split == "train":
             data_to_use = train_data
-        else: # "val" or "test" split
+        else:  # "val" or "test" split
             data_to_use = val_test_data
-    
+
         for idx, (file, label) in enumerate(data_to_use):
             yield idx, {
                 "image_file_path": file,
                 "image": file,
                 "labels": label,
-        }
-
-        
-        
+            }