Multi-threaded VisDrone and VOC downloads (#7108)

* Multi-threaded VOC download

* Update VOC.yaml

* Update

* Update general.py

* Update general.py

data/GlobalWheat2020.yaml

@@ -34,6 +34,7 @@ names: ['wheat_head']  # class names
 download: |
   from utils.general import download, Path
 
+
   # Download
   dir = Path(yaml['path'])  # dataset root dir
   urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',

data/Objects365.yaml

@@ -65,6 +65,7 @@ download: |
 
   from utils.general import Path, download, np, xyxy2xywhn
 
+
   # Make Directories
   dir = Path(yaml['path'])  # dataset root dir
   for p in 'images', 'labels':

data/SKU-110K.yaml

@@ -24,6 +24,7 @@ download: |
   from tqdm import tqdm
   from utils.general import np, pd, Path, download, xyxy2xywh
 
+
   # Download
   dir = Path(yaml['path'])  # dataset root dir
   parent = Path(dir.parent)  # download dir

data/VOC.yaml

@@ -62,7 +62,7 @@ download: |
   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
-  download(urls, dir=dir / 'images', delete=False)
+  download(urls, dir=dir / 'images', delete=False, threads=3)
 
   # Convert
   path = dir / f'images/VOCdevkit'

data/VisDrone.yaml

@@ -54,7 +54,7 @@ download: |
           'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
           'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
           'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
-  download(urls, dir=dir)
+  download(urls, dir=dir, threads=4)
 
   # Convert
   for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':

data/coco.yaml

@@ -30,6 +30,7 @@ names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 't
 download: |
   from utils.general import download, Path
 
+
   # Download labels
   segments = False  # segment or box labels
   dir = Path(yaml['path'])  # dataset root dir