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50 lines
1.8 KiB
50 lines
1.8 KiB
2 years ago
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# Parameters
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nc: 80 # number of classes
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scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
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# [depth, width, max_channels]
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l: [1.00, 1.00, 1024]
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backbone:
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# [from, repeats, module, args]
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- [-1, 1, HGStem, [32, 48]] # 0-P2/4
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- [-1, 6, HGBlock, [48, 128, 3]] # stage 1
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- [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8
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- [-1, 6, HGBlock, [96, 512, 3]] # stage 2
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- [-1, 1, DWConv, [512, 3, 2, 1, False]] # 4-P3/16
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- [-1, 6, HGBlock, [192, 1024, 5, True, False]] # cm, c2, k, light, shortcut
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- [-1, 6, HGBlock, [192, 1024, 5, True, True]]
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- [-1, 6, HGBlock, [192, 1024, 5, True, True]] # stage 3
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- [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 8-P4/32
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- [-1, 6, HGBlock, [384, 2048, 5, True, False]] # stage 4
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head:
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- [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 10 input_proj.2
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- [-1, 1, AIFI, [1024, 8]]
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- [-1, 1, Conv, [256, 1, 1]] # 12, Y5, lateral_convs.0
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- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
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- [7, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14 input_proj.1
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- [[-2, -1], 1, Concat, [1]]
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- [-1, 3, RepC3, [256]] # 16, fpn_blocks.0
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- [-1, 1, Conv, [256, 1, 1]] # 17, Y4, lateral_convs.1
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- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
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- [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 19 input_proj.0
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- [[-2, -1], 1, Concat, [1]] # cat backbone P4
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- [-1, 3, RepC3, [256]] # X3 (21), fpn_blocks.1
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- [-1, 1, Conv, [256, 3, 2]] # 22, downsample_convs.0
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- [[-1, 17], 1, Concat, [1]] # cat Y4
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- [-1, 3, RepC3, [256]] # F4 (24), pan_blocks.0
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- [-1, 1, Conv, [256, 3, 2]] # 25, downsample_convs.1
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- [[-1, 12], 1, Concat, [1]] # cat Y5
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- [-1, 3, RepC3, [256]] # F5 (27), pan_blocks.1
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- [[21, 24, 27], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
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