Open Zigars opened 3 years ago
# parameters
nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple
# anchors
anchors:
- [12,16, 19,36, 40,28] # P3/8
- [36,75, 76,55, 72,146] # P4/16
- [142,110, 192,243, 459,401] # P5/32
# yolov4s 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, BottleneckCSP, [128]],
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
[-1, 8, BottleneckCSP, [256]],
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
[-1, 8, BottleneckCSP, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
[-1, 4, BottleneckCSP, [1024]], # 10
]
# yolov4s head
# na = len(anchors[0])
head:
[[-1, 1, SPPCSP, [512]], # 11
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[8, 1, Conv, [256, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 2, BottleneckCSP2, [256]], # 16
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[6, 1, Conv, [128, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 2, BottleneckCSP2, [128]], # 21
[-1, 1, Conv, [256, 3, 1]],
[-2, 1, Conv, [256, 3, 2]],
[[-1, 16], 1, Concat, [1]], # cat
[-1, 2, BottleneckCSP2, [256]], # 25
[-1, 1, Conv, [512, 3, 1]],
[-2, 1, Conv, [512, 3, 2]],
[[-1, 11], 1, Concat, [1]], # cat
[-1, 2, BottleneckCSP2, [512]], # 29
[-1, 1, Conv, [1024, 3, 1]],
[[22,26,30], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5)
]
thanks!
thanks @WongKinYiu do you have a pretrained .pt
for yolor-s also?
@WongKinYiu whilst I'm asking, do you also have yolor-csp.pt
?
currently i only have yolor-csp.pt
for main
branch. (performance)
for paper
branch, the training will finish in two days.