Update NCTCRCHE100K.py

NCTCRCHE100K.py

@@ -46,23 +46,16 @@ class NCTCRCHE100K(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
 
-        downloaded_files = {}
-        for split, url in _URLS.items():
-            downloaded_files[split] = dl_manager.download(url)
-        
+        downloaded_files = dl_manager.download(_URLS)
+    
+        image_iters_train_norm = dl_manager.iter_archive(downloaded_files["train"])
+        image_iters_train_nonorm = dl_manager.iter_archive(downloaded_files["train_nonorm"])
+        image_iters_val = dl_manager.iter_archive(downloaded_files["val"])
+    
         return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"images": dl_manager.iter_archive(downloaded_files["train"]), "split": "train"}
-            ),
-            datasets.SplitGenerator(
-                name="train_nonorm",
-                gen_kwargs={"images": dl_manager.iter_archive(downloaded_files["train_nonorm"]), "split": "train_nonorm"}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"images": dl_manager.iter_archive(downloaded_files["val"]), "split": "val"}
-            ),
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"images": image_iters_train_norm}),
+            datasets.SplitGenerator(name="train_nonorm", gen_kwargs={"images": image_iters_train_nonorm}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"images": image_iters_val}),
         ]
 
     def _generate_examples(self, images):