Dataset autodownload fstring updates

data/Argoverse.yaml

@@ -32,7 +32,7 @@ download: |
       for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."):
           img_id = annot['image_id']
           img_name = a['images'][img_id]['name']
-          img_label_name = img_name[:-3] + "txt"
+          img_label_name = f'{img_name[:-3]}txt'
 
           cls = annot['category_id']  # instance class id
           x_center, y_center, width, height = annot['bbox']

data/VOC.yaml

@@ -59,13 +59,13 @@ download: |
   # Download
   dir = Path(yaml['path'])  # dataset root dir
   url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
-  urls = [url + 'VOCtrainval_06-Nov-2007.zip',  # 446MB, 5012 images
-          url + 'VOCtest_06-Nov-2007.zip',  # 438MB, 4953 images
-          url + 'VOCtrainval_11-May-2012.zip']  # 1.95GB, 17126 images
+  urls = [f'{url}VOCtrainval_06-Nov-2007.zip',  # 446MB, 5012 images
+          f'{url}VOCtest_06-Nov-2007.zip',  # 438MB, 4953 images
+          f'{url}VOCtrainval_11-May-2012.zip']  # 1.95GB, 17126 images
   download(urls, dir=dir / 'images', delete=False, curl=True, threads=3)
 
   # Convert
-  path = dir / f'images/VOCdevkit'
+  path = dir / 'images/VOCdevkit'
   for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
       imgs_path = dir / 'images' / f'{image_set}{year}'
       lbs_path = dir / 'labels' / f'{image_set}{year}'