Update renovation.py

renovation.py

@@ -21,7 +21,7 @@ _CITATION = """\
 
 _DESCRIPTION = """\
 Renovations is a dataset of images of houses taken in the field using smartphone
-cameras. It consists of 7 classes: Not Applicable, Very Poor, Poor, Fair, Good, Excellent, and Exceptional renovations.
+cameras. It consists of 7 classes: Not Applicable, Very Poor, Poor, Fair, Good, Great and Excellent renovations.
 Data was collected by the your research lab.
 """
 
@@ -82,19 +82,25 @@ class Renovations(datasets.GeneratorBasedBuilder):
         ]
 
     def _generate_examples(self, data_files, split):
-        all_files_and_labels = []
+        # Separate data by class
+        data_by_class = {label: [] for label in _NAMES}
         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)
+            files = [os.path.join(path, f) for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]
+            data_by_class[label].extend((file, label) for file in files)
     
+        # Shuffle and split data for each class
         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 * 0.8)]
-        test_data = all_files_and_labels[int(num_files * 0.8):int(num_files * 0.9)]
-        val_data = all_files_and_labels[int(num_files * 0.9):]
+        train_data, test_data, val_data = [], [], []
+        for label, files_and_labels in data_by_class.items():
+            random.shuffle(files_and_labels)
+            num_files = len(files_and_labels)
+            train_end = int(num_files * 0.8)
+            test_end = int(num_files * 0.9)
+            train_data.extend(files_and_labels[:train_end])
+            test_data.extend(files_and_labels[train_end:test_end])
+            val_data.extend(files_and_labels[test_end:])
     
+        # Select the appropriate split
         if split == "train":
             data_to_use = train_data
         elif split == "test":
@@ -102,6 +108,7 @@ class Renovations(datasets.GeneratorBasedBuilder):
         else:  # "val" split
             data_to_use = val_data
     
+        # Yield examples
         for idx, (file, label) in enumerate(data_to_use):
             yield idx, {
                 "image_file_path": file,