update yolov8-lite

Author: derronqi

ultralytics/__init__.py

@@ -1,9 +1,9 @@
-# Ultralytics YOLO 🚀, GPL-3.0 license
-
-__version__ = '8.0.66'
-
-from ultralytics.hub import start
-from ultralytics.yolo.engine.model import YOLO
-from ultralytics.yolo.utils.checks import check_yolo as checks
-
-__all__ = '__version__', 'YOLO', 'checks', 'start'  # allow simpler import
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+__version__ = '8.0.85'
+
+from ultralytics.hub import start
+from ultralytics.yolo.engine.model import YOLO
+from ultralytics.yolo.utils.checks import check_yolo as checks
+
+__all__ = '__version__', 'YOLO', 'checks', 'start'  # allow simpler import

ultralytics/__pycache__/__init__.cpython-38.pyc

@@ -1,4 +1,4 @@
-# Ultralytics YOLO 🚀, GPL-3.0 license
+# Ultralytics YOLO 🚀, AGPL-3.0 license
 # Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
 # Example usage: yolo train data=Argoverse.yaml
 # parent

ultralytics/datasets/Argoverse.yaml

@@ -1,4 +1,4 @@
-# Ultralytics YOLO 🚀, GPL-3.0 license
+# Ultralytics YOLO 🚀, AGPL-3.0 license
 # Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
 # Example usage: yolo train data=GlobalWheat2020.yaml
 # parent

ultralytics/datasets/GlobalWheat2020.yaml

@@ -1,4 +1,4 @@
-# Ultralytics YOLO 🚀, GPL-3.0 license
+# Ultralytics YOLO 🚀, AGPL-3.0 license
 # ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
 # Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
 # Example usage: yolo train task=classify data=imagenet

ultralytics/datasets/ImageNet.yaml

@@ -1,4 +1,4 @@
-# Ultralytics YOLO 🚀, GPL-3.0 license
+# Ultralytics YOLO 🚀, AGPL-3.0 license
 # Objects365 dataset https://www.objects365.org/ by Megvii
 # Example usage: yolo train data=Objects365.yaml
 # parent

ultralytics/datasets/Objects365.yaml

@@ -1,4 +1,4 @@
-# Ultralytics YOLO 🚀, GPL-3.0 license
+# Ultralytics YOLO 🚀, AGPL-3.0 license
 # SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
 # Example usage: yolo train data=SKU-110K.yaml
 # parent

ultralytics/datasets/SKU-110K.yaml

@@ -1,4 +1,4 @@
-# Ultralytics YOLO 🚀, GPL-3.0 license
+# Ultralytics YOLO 🚀, AGPL-3.0 license
 # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
 # Example usage: yolo train data=VOC.yaml
 # parent

ultralytics/datasets/VOC.yaml

@@ -1,4 +1,4 @@
-# Ultralytics YOLO 🚀, GPL-3.0 license
+# Ultralytics YOLO 🚀, AGPL-3.0 license
 # VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
 # Example usage: yolo train data=VisDrone.yaml
 # parent
@@ -56,7 +56,7 @@ download: |
                   cls = int(row[5]) - 1
                   box = convert_box(img_size, tuple(map(int, row[:4])))
                   lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
-                  with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
+                  with open(str(f).replace(f'{os.sep}annotations{os.sep}', f'{os.sep}labels{os.sep}'), 'w') as fl:
                       fl.writelines(lines)  # write label.txt
 
 

ultralytics/datasets/VisDrone.yaml

@@ -1,4 +1,4 @@
-# Ultralytics YOLO 🚀, GPL-3.0 license
+# Ultralytics YOLO 🚀, AGPL-3.0 license
 # COCO 2017 dataset http://cocodataset.org by Microsoft
 # Example usage: yolo train data=coco-pose.yaml
 # parent