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yolov3-tiny.yaml
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yolov3-tiny.yaml
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# parameters
number_classes: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [10,14, 23,27, 37,58] # P4/16
- [81,82, 135,169, 344,319] # P5/32
# Darknet-19 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [16, 3, 1]], # 0
[-1, 1, Maxpool, [2, 2]], # 1-P1/2
[-1, 1, Conv, [32, 3, 1]],
[-1, 1, Maxpool, [2, 2]], # 3-P2/4
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Maxpool, [2, 2]], # 5-P3/8
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Maxpool, [2, 2]], # 7-P4/16
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Maxpool, [2, 2]], # 9-P5/32
[-1, 1, Conv, [512, 3, 1]],
# [-1, 1, Maxpool, [2, 1]], fix ` Expected more than 1 value per channel when training, got input size torch.Size([1, 1024, 1, 1])` error
[-1, 1, Conv, [1024, 3, 1]], # 11
]
# YOLOv3-tiny head
head:
[[-1, 1, Bottleneck, [512, False]], # 12
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
[[-1, 8], 1, Concat, [1]], # concat backbone P4
[-1, 1, Conv, [256, 1, 1]], # 16
[[16, 12], 1, Detect, [number_classes, anchors]], # Detect(P4, P5)
]