47 lines
1.5 KiB
YAML
47 lines
1.5 KiB
YAML
# Ultralytics YOLO 🚀, AGPL-3.0 license
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# YOLOv3 object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/yolov3
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# Parameters
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nc: 80 # number of classes
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depth_multiple: 1.0 # model depth multiple
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width_multiple: 1.0 # layer channel multiple
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# darknet53 backbone
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backbone:
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# [from, number, module, args]
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- [-1, 1, Conv, [32, 3, 1]] # 0
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- [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
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- [-1, 1, Bottleneck, [64]]
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- [-1, 1, Conv, [128, 3, 2]] # 3-P2/4
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- [-1, 2, Bottleneck, [128]]
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- [-1, 1, Conv, [256, 3, 2]] # 5-P3/8
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- [-1, 8, Bottleneck, [256]]
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- [-1, 1, Conv, [512, 3, 2]] # 7-P4/16
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- [-1, 8, Bottleneck, [512]]
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- [-1, 1, Conv, [1024, 3, 2]] # 9-P5/32
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- [-1, 4, Bottleneck, [1024]] # 10
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# YOLOv3 head
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head:
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- [-1, 1, Bottleneck, [1024, False]]
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- [-1, 1, Conv, [512, 1, 1]]
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- [-1, 1, Conv, [1024, 3, 1]]
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- [-1, 1, Conv, [512, 1, 1]]
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- [-1, 1, Conv, [1024, 3, 1]] # 15 (P5/32-large)
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- [-2, 1, Conv, [256, 1, 1]]
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- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
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- [[-1, 8], 1, Concat, [1]] # cat backbone P4
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- [-1, 1, Bottleneck, [512, False]]
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- [-1, 1, Bottleneck, [512, False]]
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- [-1, 1, Conv, [256, 1, 1]]
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- [-1, 1, Conv, [512, 3, 1]] # 22 (P4/16-medium)
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- [-2, 1, Conv, [128, 1, 1]]
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- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
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- [[-1, 6], 1, Concat, [1]] # cat backbone P3
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- [-1, 1, Bottleneck, [256, False]]
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- [-1, 2, Bottleneck, [256, False]] # 27 (P3/8-small)
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- [[27, 22, 15], 1, Detect, [nc]] # Detect(P3, P4, P5)
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