在 Colab 上对 AlexeyAB Yolov4 Darknet 包进行测试时显示的额外 class 预测结果
Extra class prediction result shown when testing on AlexeyAB Yolov4 Darknet package on Colab
我已经从 Colab 上的 AlexeyAB Darknet 包中完成了 Yolov4 异议检测的模型训练。
（来自 https://github.com/AlexeyAB/darknet）
两个 classes 被定义为“scratch”和“blood”。
然后 运行 以下命令用于在单个图像上进行测试：
!./darknet detector test data/obj.data cfg/yolo-obj.cfg backup/yolo-object_last.weights images/meat/test/376.jpg -dont show
结果如下图。预计结果中只有一处划痕和血迹有概率显示。
然而，它显示了如此多的划痕和血液预测（这个post的最后几行）！
classes（=2）的数量，class的名字，应该已经在obj.data和yolo-obj.cfg中设置好了。
任何人都可以建议为什么以及如何解决它吗？
CUDA-version: 11010 (11020), cuDNN: 7.6.5, GPU count: 1
OpenCV version: 3.2.0
0 : compute_capability = 370, cudnn_half = 0, GPU: Tesla K80
net.optimized_memory = 0
mini_batch = 1, batch = 1, time_steps = 1, train = 0
layer filters size/strd(dil) input output
0 Create CUDA-stream - 0
Create cudnn-handle 0
conv 32 3 x 3/ 1 416 x 416 x 3 -> 416 x 416 x 32 0.299 BF
1 conv 64 3 x 3/ 2 416 x 416 x 32 -> 208 x 208 x 64 1.595 BF
2 conv 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF
3 route 1 -> 208 x 208 x 64
4 conv 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF
5 conv 32 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 32 0.177 BF
6 conv 64 3 x 3/ 1 208 x 208 x 32 -> 208 x 208 x 64 1.595 BF
7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 208 x 208 x 64 0.003 BF
8 conv 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF
9 route 8 2 -> 208 x 208 x 128
10 conv 64 1 x 1/ 1 208 x 208 x 128 -> 208 x 208 x 64 0.709 BF
11 conv 128 3 x 3/ 2 208 x 208 x 64 -> 104 x 104 x 128 1.595 BF
12 conv 64 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF
13 route 11 -> 104 x 104 x 128
14 conv 64 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF
15 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF
16 conv 64 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.797 BF
17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 104 x 104 x 64 0.001 BF
18 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF
19 conv 64 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.797 BF
20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 104 x 104 x 64 0.001 BF
21 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF
22 route 21 12 -> 104 x 104 x 128
23 conv 128 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 128 0.354 BF
24 conv 256 3 x 3/ 2 104 x 104 x 128 -> 52 x 52 x 256 1.595 BF
25 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
26 route 24 -> 52 x 52 x 256
27 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
28 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
29 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
31 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
32 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
34 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
35 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
37 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
38 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
40 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
41 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
43 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
44 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
46 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
47 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
49 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
50 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
52 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
53 route 52 25 -> 52 x 52 x 256
54 conv 256 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 256 0.354 BF
55 conv 512 3 x 3/ 2 52 x 52 x 256 -> 26 x 26 x 512 1.595 BF
56 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
57 route 55 -> 26 x 26 x 512
58 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
59 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
60 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
62 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
63 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
65 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
66 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
68 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
69 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
71 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
72 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
74 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
75 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
77 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
78 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
80 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
81 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
83 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
84 route 83 56 -> 26 x 26 x 512
85 conv 512 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 512 0.354 BF
86 conv 1024 3 x 3/ 2 26 x 26 x 512 -> 13 x 13 x1024 1.595 BF
87 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
88 route 86 -> 13 x 13 x1024
89 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
90 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
91 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF
93 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
94 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF
96 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
97 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF
99 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
100 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF
102 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
103 route 102 87 -> 13 x 13 x1024
104 conv 1024 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x1024 0.354 BF
105 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
106 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
107 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
108 max 5x 5/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.002 BF
109 route 107 -> 13 x 13 x 512
110 max 9x 9/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.007 BF
111 route 107 -> 13 x 13 x 512
112 max 13x13/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.015 BF
113 route 112 110 108 107 -> 13 x 13 x2048
114 conv 512 1 x 1/ 1 13 x 13 x2048 -> 13 x 13 x 512 0.354 BF
115 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
116 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
117 conv 256 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 256 0.044 BF
118 upsample 2x 13 x 13 x 256 -> 26 x 26 x 256
119 route 85 -> 26 x 26 x 512
120 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
121 route 120 118 -> 26 x 26 x 512
122 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
123 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
124 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
125 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
126 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
127 conv 128 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 128 0.044 BF
128 upsample 2x 26 x 26 x 128 -> 52 x 52 x 128
129 route 54 -> 52 x 52 x 256
130 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
131 route 130 128 -> 52 x 52 x 256
132 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
133 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
134 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
135 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
136 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
137 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
138 conv 21 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 21 0.029 BF
139 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20
nms_kind: greedynms (1), beta = 0.600000
140 route 136 -> 52 x 52 x 128
141 conv 256 3 x 3/ 2 52 x 52 x 128 -> 26 x 26 x 256 0.399 BF
142 route 141 126 -> 26 x 26 x 512
143 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
144 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
145 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
146 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
147 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
148 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
149 conv 21 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 21 0.015 BF
150 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10
nms_kind: greedynms (1), beta = 0.600000
151 route 147 -> 26 x 26 x 256
152 conv 512 3 x 3/ 2 26 x 26 x 256 -> 13 x 13 x 512 0.399 BF
153 route 152 116 -> 13 x 13 x1024
154 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
155 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
156 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
157 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
158 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
159 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
160 conv 21 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 21 0.007 BF
161 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05
nms_kind: greedynms (1), beta = 0.600000
Total BFLOPS 59.570
avg_outputs = 489910
Allocate additional workspace_size = 12.46 MB
Loading weights from backup/yolo-object_last.weights...
seen 64, trained: 57 K-images (0 Kilo-batches_64)
Done! Loaded 160 layers from weights-file
Detection layer: 139 - type = 28
Detection layer: 150 - type = 28
Detection layer: 161 - type = 28
images/meat/13.jpg: Predicted in 101.928000 milli-seconds.
scratch: 52%
blood: 36%
scratch: 68%
blood: 68%
scratch: 42%
blood: 42%
scratch: 60%
blood: 60%
scratch: 43%
blood: 43%
scratch: 45%
blood: 45%
blood: 48%
scratch: 42%
scratch: 45%
blood: 45%
scratch: 65%
blood: 65%
scratch: 56%
.....（下面显示更多的划痕和血迹....）
这是正常行为。
你“总是”会得到很多检测。但您也可以看到您的一些检测结果低于 50% 的置信度分数。哪些应该被丢弃。
我认为管道已经执行非最大抑制 (NMS) 以删除重复和重叠检测（不确定），但您还可以另外设置置信度分数阈值，以仅显示 50% 以上 -thresh 0.5
这将为您提供最终的预期结果。不过，如果你继续获得超过你应该获得的，好吧，这是每个人的目标:)
一切都是关于超参数调整和更多数据来训练我们的模型以提供我们想要的结果。
我已经从 Colab 上的 AlexeyAB Darknet 包中完成了 Yolov4 异议检测的模型训练。 （来自 https://github.com/AlexeyAB/darknet） 两个 classes 被定义为“scratch”和“blood”。 然后 运行 以下命令用于在单个图像上进行测试：
!./darknet detector test data/obj.data cfg/yolo-obj.cfg backup/yolo-object_last.weights images/meat/test/376.jpg -dont show
结果如下图。预计结果中只有一处划痕和血迹有概率显示。 然而，它显示了如此多的划痕和血液预测（这个post的最后几行）！ classes（=2）的数量，class的名字，应该已经在obj.data和yolo-obj.cfg中设置好了。 任何人都可以建议为什么以及如何解决它吗？
CUDA-version: 11010 (11020), cuDNN: 7.6.5, GPU count: 1
OpenCV version: 3.2.0
0 : compute_capability = 370, cudnn_half = 0, GPU: Tesla K80
net.optimized_memory = 0
mini_batch = 1, batch = 1, time_steps = 1, train = 0
layer filters size/strd(dil) input output
0 Create CUDA-stream - 0
Create cudnn-handle 0
conv 32 3 x 3/ 1 416 x 416 x 3 -> 416 x 416 x 32 0.299 BF
1 conv 64 3 x 3/ 2 416 x 416 x 32 -> 208 x 208 x 64 1.595 BF
2 conv 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF
3 route 1 -> 208 x 208 x 64
4 conv 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF
5 conv 32 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 32 0.177 BF
6 conv 64 3 x 3/ 1 208 x 208 x 32 -> 208 x 208 x 64 1.595 BF
7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 208 x 208 x 64 0.003 BF
8 conv 64 1 x 1/ 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF
9 route 8 2 -> 208 x 208 x 128
10 conv 64 1 x 1/ 1 208 x 208 x 128 -> 208 x 208 x 64 0.709 BF
11 conv 128 3 x 3/ 2 208 x 208 x 64 -> 104 x 104 x 128 1.595 BF
12 conv 64 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF
13 route 11 -> 104 x 104 x 128
14 conv 64 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF
15 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF
16 conv 64 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.797 BF
17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 104 x 104 x 64 0.001 BF
18 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF
19 conv 64 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.797 BF
20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 104 x 104 x 64 0.001 BF
21 conv 64 1 x 1/ 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF
22 route 21 12 -> 104 x 104 x 128
23 conv 128 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 128 0.354 BF
24 conv 256 3 x 3/ 2 104 x 104 x 128 -> 52 x 52 x 256 1.595 BF
25 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
26 route 24 -> 52 x 52 x 256
27 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
28 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
29 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
31 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
32 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
34 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
35 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
37 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
38 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
40 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
41 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
43 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
44 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
46 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
47 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
49 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
50 conv 128 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 52 x 52 x 128 0.000 BF
52 conv 128 1 x 1/ 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
53 route 52 25 -> 52 x 52 x 256
54 conv 256 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 256 0.354 BF
55 conv 512 3 x 3/ 2 52 x 52 x 256 -> 26 x 26 x 512 1.595 BF
56 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
57 route 55 -> 26 x 26 x 512
58 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
59 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
60 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
62 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
63 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
65 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
66 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
68 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
69 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
71 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
72 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
74 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
75 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
77 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
78 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
80 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
81 conv 256 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 26 x 26 x 256 0.000 BF
83 conv 256 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
84 route 83 56 -> 26 x 26 x 512
85 conv 512 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 512 0.354 BF
86 conv 1024 3 x 3/ 2 26 x 26 x 512 -> 13 x 13 x1024 1.595 BF
87 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
88 route 86 -> 13 x 13 x1024
89 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
90 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
91 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF
93 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
94 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF
96 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
97 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF
99 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
100 conv 512 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 13 x 13 x 512 0.000 BF
102 conv 512 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
103 route 102 87 -> 13 x 13 x1024
104 conv 1024 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x1024 0.354 BF
105 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
106 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
107 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
108 max 5x 5/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.002 BF
109 route 107 -> 13 x 13 x 512
110 max 9x 9/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.007 BF
111 route 107 -> 13 x 13 x 512
112 max 13x13/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.015 BF
113 route 112 110 108 107 -> 13 x 13 x2048
114 conv 512 1 x 1/ 1 13 x 13 x2048 -> 13 x 13 x 512 0.354 BF
115 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
116 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
117 conv 256 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 256 0.044 BF
118 upsample 2x 13 x 13 x 256 -> 26 x 26 x 256
119 route 85 -> 26 x 26 x 512
120 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
121 route 120 118 -> 26 x 26 x 512
122 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
123 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
124 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
125 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
126 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
127 conv 128 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 128 0.044 BF
128 upsample 2x 26 x 26 x 128 -> 52 x 52 x 128
129 route 54 -> 52 x 52 x 256
130 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
131 route 130 128 -> 52 x 52 x 256
132 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
133 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
134 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
135 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
136 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
137 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
138 conv 21 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 21 0.029 BF
139 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20
nms_kind: greedynms (1), beta = 0.600000
140 route 136 -> 52 x 52 x 128
141 conv 256 3 x 3/ 2 52 x 52 x 128 -> 26 x 26 x 256 0.399 BF
142 route 141 126 -> 26 x 26 x 512
143 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
144 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
145 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
146 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
147 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
148 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
149 conv 21 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 21 0.015 BF
150 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10
nms_kind: greedynms (1), beta = 0.600000
151 route 147 -> 26 x 26 x 256
152 conv 512 3 x 3/ 2 26 x 26 x 256 -> 13 x 13 x 512 0.399 BF
153 route 152 116 -> 13 x 13 x1024
154 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
155 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
156 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
157 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
158 conv 512 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
159 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
160 conv 21 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 21 0.007 BF
161 yolo
[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05
nms_kind: greedynms (1), beta = 0.600000
Total BFLOPS 59.570
avg_outputs = 489910
Allocate additional workspace_size = 12.46 MB
Loading weights from backup/yolo-object_last.weights...
seen 64, trained: 57 K-images (0 Kilo-batches_64)
Done! Loaded 160 layers from weights-file
Detection layer: 139 - type = 28
Detection layer: 150 - type = 28
Detection layer: 161 - type = 28
images/meat/13.jpg: Predicted in 101.928000 milli-seconds.
scratch: 52%
blood: 36%
scratch: 68%
blood: 68%
scratch: 42%
blood: 42%
scratch: 60%
blood: 60%
scratch: 43%
blood: 43%
scratch: 45%
blood: 45%
blood: 48%
scratch: 42%
scratch: 45%
blood: 45%
scratch: 65%
blood: 65%
scratch: 56%
.....（下面显示更多的划痕和血迹....）
这是正常行为。
你“总是”会得到很多检测。但您也可以看到您的一些检测结果低于 50% 的置信度分数。哪些应该被丢弃。
我认为管道已经执行非最大抑制 (NMS) 以删除重复和重叠检测（不确定），但您还可以另外设置置信度分数阈值，以仅显示 50% 以上 -thresh 0.5
这将为您提供最终的预期结果。不过，如果你继续获得超过你应该获得的，好吧，这是每个人的目标:)
一切都是关于超参数调整和更多数据来训练我们的模型以提供我们想要的结果。