请问论文中表Ⅶ的各算法在多模态下的数据结果您是如何实现的，因为我并没有找到公开的算法代码 about superyolo HOT 2 CLOSED

如果可以的话，能否请您分享一份YOLOrs和YOLO Fusion的代码，我的邮箱是[email protected]

from superyolo.

我按照论文的结构复现的，这是YOLOrs的yaml文件

#20210318 zjq
#parameters
nc: 8 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple

#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

steam:
[[-1, 1, Conv, [32, 3, 1]], # 0
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
[-1, 1, Bottleneck, [64]],
[-1, 1, Conv, [32, 1, 1]],
]

backbone:
#[from, number, module, args]
[
[-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]],
]

#YOLOv3 head
head:
[ #[-1, 1, Bottleneck, [1024, False]],
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [14, 1, 1]], # (P4/128-large)

[-2, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 3], 1, Concat, [1]], # cat backbone P3 6
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [14, 1, 1]], # (P4/64-medium)

[-2, 1, Conv, [64, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[3, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, -2], 1, Concat, [1]], # cat backbone P3 6
[-1, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [14, 1, 1]], # (P3/32 -small)

[[33, 22, 12], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 27 22 15
]

from superyolo.