Calculate mean Average Precision (mAP) and confusion matrix for object detection models. Bounding box information for groundtruth and prediction is YOLO training dataset format.
License: Other
Hello! Can you please tell me, yolov4-tiny or yolov3-tiny are supported in your project? I can't detect any object. I use custom configs
Try to load cfg: /content/gdrive/MyDrive/YOLO_metric/cfg/yolov4-custom.cfg, weights: /content/gdrive/MyDrive/YOLO_metric/data/mydata/darknet3_zoo/darknet_train_recycle09_run00/backup/yolov4-custom_best.weights, clear = 0
compute_capability = 370, cudnn_half = 0
layer filters size input output
0 Couldn't find activation function mish, going with ReLU
conv 32 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 32 0.299 BF
1 Couldn't find activation function mish, going with ReLU
conv 64 3 x 3 / 2 416 x 416 x 32 -> 208 x 208 x 64 1.595 BF
2 Couldn't find activation function mish, going with ReLU
conv 64 1 x 1 / 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF
3 route 1
4 Couldn't find activation function mish, going with ReLU
conv 64 1 x 1 / 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF
5 Couldn't find activation function mish, going with ReLU
conv 32 1 x 1 / 1 208 x 208 x 64 -> 208 x 208 x 32 0.177 BF
6 Couldn't find activation function mish, going with ReLU
conv 64 3 x 3 / 1 208 x 208 x 32 -> 208 x 208 x 64 1.595 BF
7 Shortcut Layer: 4
8 Couldn't find activation function mish, going with ReLU
conv 64 1 x 1 / 1 208 x 208 x 64 -> 208 x 208 x 64 0.354 BF
9 route 8 2
10 Couldn't find activation function mish, going with ReLU
conv 64 1 x 1 / 1 208 x 208 x 128 -> 208 x 208 x 64 0.709 BF
11 Couldn't find activation function mish, going with ReLU
conv 128 3 x 3 / 2 208 x 208 x 64 -> 104 x 104 x 128 1.595 BF
12 Couldn't find activation function mish, going with ReLU
conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF
13 route 11
14 Couldn't find activation function mish, going with ReLU
conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF
15 Couldn't find activation function mish, going with ReLU
conv 64 1 x 1 / 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF
16 Couldn't find activation function mish, going with ReLU
conv 64 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 64 0.797 BF
17 Shortcut Layer: 14
18 Couldn't find activation function mish, going with ReLU
conv 64 1 x 1 / 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF
19 Couldn't find activation function mish, going with ReLU
conv 64 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 64 0.797 BF
20 Shortcut Layer: 17
21 Couldn't find activation function mish, going with ReLU
conv 64 1 x 1 / 1 104 x 104 x 64 -> 104 x 104 x 64 0.089 BF
22 route 21 12
23 Couldn't find activation function mish, going with ReLU
conv 128 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 128 0.354 BF
24 Couldn't find activation function mish, going with ReLU
conv 256 3 x 3 / 2 104 x 104 x 128 -> 52 x 52 x 256 1.595 BF
25 Couldn't find activation function mish, going with ReLU
conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
26 route 24
27 Couldn't find activation function mish, going with ReLU
conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
28 Couldn't find activation function mish, going with ReLU
conv 128 1 x 1 / 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
29 Couldn't find activation function mish, going with ReLU
conv 128 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
30 Shortcut Layer: 27
31 Couldn't find activation function mish, going with ReLU
conv 128 1 x 1 / 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
32 Couldn't find activation function mish, going with ReLU
conv 128 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
33 Shortcut Layer: 30
34 Couldn't find activation function mish, going with ReLU
conv 128 1 x 1 / 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
35 Couldn't find activation function mish, going with ReLU
conv 128 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
36 Shortcut Layer: 33
37 Couldn't find activation function mish, going with ReLU
conv 128 1 x 1 / 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
38 Couldn't find activation function mish, going with ReLU
conv 128 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
39 Shortcut Layer: 36
40 Couldn't find activation function mish, going with ReLU
conv 128 1 x 1 / 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
41 Couldn't find activation function mish, going with ReLU
conv 128 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
42 Shortcut Layer: 39
43 Couldn't find activation function mish, going with ReLU
conv 128 1 x 1 / 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
44 Couldn't find activation function mish, going with ReLU
conv 128 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
45 Shortcut Layer: 42
46 Couldn't find activation function mish, going with ReLU
conv 128 1 x 1 / 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
47 Couldn't find activation function mish, going with ReLU
conv 128 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
48 Shortcut Layer: 45
49 Couldn't find activation function mish, going with ReLU
conv 128 1 x 1 / 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
50 Couldn't find activation function mish, going with ReLU
conv 128 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 128 0.797 BF
51 Shortcut Layer: 48
52 Couldn't find activation function mish, going with ReLU
conv 128 1 x 1 / 1 52 x 52 x 128 -> 52 x 52 x 128 0.089 BF
53 route 52 25
54 Couldn't find activation function mish, going with ReLU
conv 256 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 256 0.354 BF
55 Couldn't find activation function mish, going with ReLU
conv 512 3 x 3 / 2 52 x 52 x 256 -> 26 x 26 x 512 1.595 BF
56 Couldn't find activation function mish, going with ReLU
conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
57 route 55
58 Couldn't find activation function mish, going with ReLU
conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
59 Couldn't find activation function mish, going with ReLU
conv 256 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
60 Couldn't find activation function mish, going with ReLU
conv 256 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
61 Shortcut Layer: 58
62 Couldn't find activation function mish, going with ReLU
conv 256 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
63 Couldn't find activation function mish, going with ReLU
conv 256 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
64 Shortcut Layer: 61
65 Couldn't find activation function mish, going with ReLU
conv 256 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
66 Couldn't find activation function mish, going with ReLU
conv 256 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
67 Shortcut Layer: 64
68 Couldn't find activation function mish, going with ReLU
conv 256 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
69 Couldn't find activation function mish, going with ReLU
conv 256 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
70 Shortcut Layer: 67
71 Couldn't find activation function mish, going with ReLU
conv 256 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
72 Couldn't find activation function mish, going with ReLU
conv 256 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
73 Shortcut Layer: 70
74 Couldn't find activation function mish, going with ReLU
conv 256 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
75 Couldn't find activation function mish, going with ReLU
conv 256 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
76 Shortcut Layer: 73
77 Couldn't find activation function mish, going with ReLU
conv 256 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
78 Couldn't find activation function mish, going with ReLU
conv 256 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
79 Shortcut Layer: 76
80 Couldn't find activation function mish, going with ReLU
conv 256 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
81 Couldn't find activation function mish, going with ReLU
conv 256 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.797 BF
82 Shortcut Layer: 79
83 Couldn't find activation function mish, going with ReLU
conv 256 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 256 0.089 BF
84 route 83 56
85 Couldn't find activation function mish, going with ReLU
conv 512 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 512 0.354 BF
86 Couldn't find activation function mish, going with ReLU
conv 1024 3 x 3 / 2 26 x 26 x 512 -> 13 x 13 x1024 1.595 BF
87 Couldn't find activation function mish, going with ReLU
conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
88 route 86
89 Couldn't find activation function mish, going with ReLU
conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF
90 Couldn't find activation function mish, going with ReLU
conv 512 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
91 Couldn't find activation function mish, going with ReLU
conv 512 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
92 Shortcut Layer: 89
93 Couldn't find activation function mish, going with ReLU
conv 512 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
94 Couldn't find activation function mish, going with ReLU
conv 512 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
95 Shortcut Layer: 92
96 Couldn't find activation function mish, going with ReLU
conv 512 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
97 Couldn't find activation function mish, going with ReLU
conv 512 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
98 Shortcut Layer: 95
99 Couldn't find activation function mish, going with ReLU
conv 512 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
100 Couldn't find activation function mish, going with ReLU
conv 512 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.797 BF
101 Shortcut Layer: 98
102 Couldn't find activation function mish, going with ReLU
conv 512 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.089 BF
103 route 102 87
104 Couldn't find activation function mish, going with ReLU
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 5 x 5 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.002 BF
109 route 107
110 max 9 x 9 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.007 BF
111 route 107
112 max 13 x 13 / 1 13 x 13 x 512 -> 13 x 13 x 512 0.015 BF
113 route 112 110 108 107
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
120 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
121 route 120 118
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
130 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
131 route 130 128
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
Unused field: 'scale_x_y = 1.2'
Unused field: 'iou_thresh = 0.213'
Unused field: 'cls_normalizer = 1.0'
Unused field: 'iou_normalizer = 0.07'
Unused field: 'iou_loss = ciou'
Unused field: 'nms_kind = greedynms'
Unused field: 'beta_nms = 0.6'
Unused field: 'max_delta = 5'
140 route 136
141 conv 256 3 x 3 / 2 52 x 52 x 128 -> 26 x 26 x 256 0.399 BF
142 route 141 126
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
Unused field: 'scale_x_y = 1.1'
Unused field: 'iou_thresh = 0.213'
Unused field: 'cls_normalizer = 1.0'
Unused field: 'iou_normalizer = 0.07'
Unused field: 'iou_loss = ciou'
Unused field: 'nms_kind = greedynms'
Unused field: 'beta_nms = 0.6'
Unused field: 'max_delta = 5'
151 route 147
152 conv 512 3 x 3 / 2 26 x 26 x 256 -> 13 x 13 x 512 0.399 BF
153 route 152 116
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
Unused field: 'scale_x_y = 1.05'
Unused field: 'iou_thresh = 0.213'
Unused field: 'cls_normalizer = 1.0'
Unused field: 'iou_normalizer = 0.07'
Unused field: 'iou_loss = ciou'
Unused field: 'nms_kind = greedynms'
Unused field: 'beta_nms = 0.6'
Unused field: 'max_delta = 5'
Total BFLOPS 59.562
Allocate additional workspace_size = 99.68 MB
Loading weights from /content/gdrive/MyDrive/YOLO_metric/data/mydata/darknet3_zoo/darknet_train_recycle09_run00/backup/yolov4-custom_best.weights...
seen 64
CUDA status Error: file: ./src/convolutional_kernels.cu : () : line: 143 : build time: Sep 11 2021 - 11:21:20
CUDA Error: no kernel image is available for execution on the device
python3: : Unknown error -1373889909
Dear Contributor,
I have used your repository to get a confusion matrix for the YOLOv4 OD model. However, when I executed this code "python modulized/save_label_as_yolo_format.py" it is taking more time to give the inference. Kindly advise what needs to be done to expedite the process.
where is your class mapmetrix
I wanted to use this method by calling 'python3 modulized/compare_simple.py', but I have got an error:
'TypeError: * wants int'
for file 'modulized/module/metric_module.py' in the line 234:
"content2 += "%*sPrediction\n"%(12+(len(content2)-10)/2,"")"
The problem can be that '12+(len(content2)-10)/2' is not necessarily an integer.
My solution is to rewrite line 234 to:
"content2 += "%*sPrediction\n"%(round(12+(len(content2)-10)/2),"")"
But there could be other solutions as well
Hi @whynotw can you please tell me more elaborately about data setting on how to set if i have ground truth and prediction coordinates.. for two or more classes..
Hi, I have tried to use this method but encountered an error while running the modulized/save_label_as_yolo_format.py file.
Here I have an error in the line 128 saying: "KeyError: b'category_name'"
Line 128 is: 'label = DICT_CLASS[result[0]]'
This error is due to the fact that for some reasons result[0] is type of binary string, but in DICT_CLASS the elements are of type string.
The solution is to modify 'label = DICT_CLASS[result[0]]' to 'label = DICT_CLASS[result[0].decode()]'
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