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models/yolov3-spp.yaml

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+# parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # expand model depth
+width_multiple: 1.0  # expand layer channels
+
+# anchors
+anchors:
+  - [10,13, 16,30, 33,23]  # P3/8
+  - [30,61, 62,45, 59,119]  # P4/16
+  - [116,90, 156,198, 373,326]  # P5/32
+
+# darknet53 backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Conv, [32, 3, 1]],  # 0
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2
+   [-1, 1, Bottleneck, [64]],
+   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4
+   [-1, 2, Bottleneck, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 5-P3/8
+   [-1, 8, Bottleneck, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 7-P4/16
+   [-1, 8, Bottleneck, [512]],
+   [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
+   [-1, 4, Bottleneck, [1024]],  # 10
+  ]
+
+# yolov3-spp head
+# na = len(anchors[0])
+head:
+  [[-1, 1, Bottleneck, [1024, False]],  # 11
+   [-1, 1, SPP, [512, [5, 9, 13]]],
+   [-1, 1, Conv, [1024, 3, 1]],
+   [-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, Conv, [1024, 3, 1]],
+   [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]],  # 16 (P5/32-large)
+
+   [-3, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P4
+   [-1, 1, Bottleneck, [512, False]],
+   [-1, 1, Bottleneck, [512, False]],
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [512, 3, 1]],
+   [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]],  # 24 (P4/16-medium)
+
+   [-3, 1, Conv, [128, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P3
+   [-1, 1, Bottleneck, [256, False]],
+   [-1, 2, Bottleneck, [256, False]],
+   [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]],  # 30 (P3/8-small)
+
+   [[], 1, Detect, [nc, anchors]],   # Detect(P3, P4, P5)
+  ]