Commit
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f8e1148
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Parent(s):
8acb573
Update dataset headers (#4162)
Browse files- data/Argoverse_HD.yaml +6 -5
- data/GlobalWheat2020.yaml +6 -5
- data/Objects365.yaml +6 -5
- data/SKU-110K.yaml +6 -5
- data/VOC.yaml +7 -6
- data/VisDrone.yaml +6 -5
- data/coco.yaml +6 -5
- data/coco128.yaml +7 -6
- data/xView.yaml +7 -6
data/Argoverse_HD.yaml
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# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
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# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
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# YOLOv5 π example usage: python train.py --data Argoverse_HD.yaml
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# parent
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# βββ yolov5
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# βββ datasets
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# βββ Argoverse β downloads here
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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data/GlobalWheat2020.yaml
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# Global Wheat 2020 dataset http://www.global-wheat.com/
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
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# Global Wheat 2020 dataset http://www.global-wheat.com/
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# YOLOv5 π example usage: python train.py --data GlobalWheat2020.yaml
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# parent
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# βββ yolov5
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# βββ datasets
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# βββ GlobalWheat2020 β downloads here
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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data/Objects365.yaml
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# Objects365 dataset https://www.objects365.org/
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
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# Objects365 dataset https://www.objects365.org/
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# YOLOv5 π example usage: python train.py --data Objects365.yaml
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# parent
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# βββ yolov5
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# βββ datasets
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# βββ Objects365 β downloads here
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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data/SKU-110K.yaml
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# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
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# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19
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# YOLOv5 π example usage: python train.py --data SKU-110K.yaml
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# parent
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# βββ yolov5
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# βββ datasets
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# βββ SKU-110K β downloads here
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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data/VOC.yaml
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
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# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC
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# YOLOv5 π example usage: python train.py --data VOC.yaml
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# parent
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# βββ yolov5
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# βββ datasets
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# βββ VOC β downloads here
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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data/VisDrone.yaml
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# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
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# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset
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# YOLOv5 π example usage: python train.py --data VisDrone.yaml
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# parent
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# βββ yolov5
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# βββ datasets
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# βββ VisDrone β downloads here
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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data/coco.yaml
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# COCO 2017 dataset http://cocodataset.org
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
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# COCO 2017 dataset http://cocodataset.org
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# YOLOv5 π example usage: python train.py --data coco.yaml
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# parent
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# βββ yolov5
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# βββ datasets
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# βββ coco β downloads here
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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data/coco128.yaml
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
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# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
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# YOLOv5 π example usage: python train.py --data coco128.yaml
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# parent
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# βββ yolov5
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# βββ datasets
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# βββ coco128 β downloads here
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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data/xView.yaml
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# xView 2018 dataset https://challenge.xviewdataset.org
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
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# xView 2018 dataset https://challenge.xviewdataset.org
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# -------- DOWNLOAD DATA MANUALLY from URL above and unzip to 'datasets/xView' before running train command! --------
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# YOLOv5 π example usage: python train.py --data xView.yaml
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# parent
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# βββ yolov5
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# βββ datasets
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# βββ xView β downloads here
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# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
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