39 lines
1.3 KiB
YAML
39 lines
1.3 KiB
YAML
# Ultralytics YOLO 🚀, AGPL-3.0 license
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# YOLOv9m object detection model. For Usage examples see https://docs.ultralytics.com/models/yolov9
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# 603 layers, 20216160 parameters, 77.9 GFLOPs
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# Parameters
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nc: 80 # number of classes
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# GELAN backbone
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backbone:
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- [-1, 1, Conv, [32, 3, 2]] # 0-P1/2
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- [-1, 1, Conv, [64, 3, 2]] # 1-P2/4
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- [-1, 1, RepNCSPELAN4, [128, 128, 64, 1]] # 2
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- [-1, 1, AConv, [240]] # 3-P3/8
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- [-1, 1, RepNCSPELAN4, [240, 240, 120, 1]] # 4
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- [-1, 1, AConv, [360]] # 5-P4/16
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- [-1, 1, RepNCSPELAN4, [360, 360, 180, 1]] # 6
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- [-1, 1, AConv, [480]] # 7-P5/32
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- [-1, 1, RepNCSPELAN4, [480, 480, 240, 1]] # 8
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- [-1, 1, SPPELAN, [480, 240]] # 9
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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, 1, RepNCSPELAN4, [360, 360, 180, 1]] # 12
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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, 1, RepNCSPELAN4, [240, 240, 120, 1]] # 15
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- [-1, 1, AConv, [180]]
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- [[-1, 12], 1, Concat, [1]] # cat head P4
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- [-1, 1, RepNCSPELAN4, [360, 360, 180, 1]] # 18 (P4/16-medium)
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- [-1, 1, AConv, [240]]
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- [[-1, 9], 1, Concat, [1]] # cat head P5
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- [-1, 1, RepNCSPELAN4, [480, 480, 240, 1]] # 21 (P5/32-large)
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- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
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