59 lines
2.5 KiB
YAML
59 lines
2.5 KiB
YAML
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# DOTA8 dataset 8 images from split DOTAv1 dataset by Ultralytics
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# Documentation: https://docs.ultralytics.com/datasets/obb/dota8/
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# Example usage: yolo train model=yolov8n-obb.pt data=dota8.yaml
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# parent
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# ├── ultralytics
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# └── datasets
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# └── dota8 ← downloads here (1MB)
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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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path: /media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/point2
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train: train # 数据集路径下的train.txt
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val: val # 数据集路径下的val.txt
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test: test # 数据集路径下的test.txt
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nc: 1 # number of classes
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kpt_shape: [4, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
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scales: # model compound scaling constants, i.e. 'model=yolo11n-pose.yaml' will call yolo11.yaml with scale 'n'
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# [depth, width, max_channels]
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n: [0.50, 0.25, 1024] # summary: 344 layers, 2908507 parameters, 2908491 gradients, 7.7 GFLOPs
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s: [0.50, 0.50, 1024] # summary: 344 layers, 9948811 parameters, 9948795 gradients, 23.5 GFLOPs
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m: [0.50, 1.00, 512] # summary: 434 layers, 20973273 parameters, 20973257 gradients, 72.3 GFLOPs
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l: [1.00, 1.00, 512] # summary: 656 layers, 26230745 parameters, 26230729 gradients, 91.4 GFLOPs
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x: [1.00, 1.50, 512] # summary: 656 layers, 58889881 parameters, 58889865 gradients, 204.3 GFLOPs
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# YOLO11n backbone
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backbone:
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# [from, repeats, module, args]
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- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
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- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
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- [-1, 2, C3k2, [256, False, 0.25]]
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- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
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- [-1, 2, C3k2, [512, False, 0.25]]
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- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
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- [-1, 2, C3k2, [512, True]]
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- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
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- [-1, 2, C3k2, [1024, True]]
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- [-1, 1, SPPF, [1024, 5]] # 9
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- [-1, 2, C2PSA, [1024]] # 10
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# YOLO11n head
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head:
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- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
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- [[-1, 6], 1, Concat, [1]] # cat backbone P4
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- [-1, 2, C3k2, [512, False]] # 13
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- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
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- [[-1, 4], 1, Concat, [1]] # cat backbone P3
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- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
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- [-1, 1, Conv, [256, 3, 2]]
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- [[-1, 13], 1, Concat, [1]] # cat head P4
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- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
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- [-1, 1, Conv, [512, 3, 2]]
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- [[-1, 10], 1, Concat, [1]] # cat head P5
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- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
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- [[16, 19, 22], 1, Pose, [nc, kpt_shape]] # Detect(P3, P4, P5)
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