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NEW: release DJI Payload-SDK version 3.12.0

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DJI
2025-06-27 22:36:34 +08:00
parent 54b9f6c6c1
commit 326b8698dd
381 changed files with 122574 additions and 451 deletions
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6
samples/sample_c++/module_sample/liveview/data/yolo-fastest-1.1_coco/coco.data Normal file
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classes= 80
train = ~/COCO/train2017.txt
valid = ~/COCO/val2017.txt
names = coco.names
backup = model

80
samples/sample_c++/module_sample/liveview/data/yolo-fastest-1.1_coco/coco.names Normal file
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person
bicycle
car
motorbike
aeroplane
bus
train
truck
boat
traffic light
fire hydrant
stop sign
parking meter
bench
bird
cat
dog
horse
sheep
cow
elephant
bear
zebra
giraffe
backpack
umbrella
handbag
tie
suitcase
frisbee
skis
snowboard
sports ball
kite
baseball bat
baseball glove
skateboard
surfboard
tennis racket
bottle
wine glass
cup
fork
knife
spoon
bowl
banana
apple
sandwich
orange
broccoli
carrot
hot dog
pizza
donut
cake
chair
sofa
pottedplant
bed
diningtable
toilet
tvmonitor
laptop
mouse
remote
keyboard
cell phone
microwave
oven
toaster
sink
refrigerator
book
clock
vase
scissors
teddy bear
hair drier
toothbrush

244
samples/sample_c++/module_sample/liveview/data/yolo-fastest-1.1_coco/mAP_log.txt Normal file
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CUDA-version: 10010 (10010), cuDNN: 7.6.5, GPU count: 4
OpenCV version: 4.9.1
0,1,2,3
0 : compute_capability = 610, cudnn_half = 0, GPU: GeForce GTX 1080 Ti
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 8 3 x 3/ 2 320 x 320 x 3 -> 160 x 160 x 8 0.011 BF
1 conv 8 1 x 1/ 1 160 x 160 x 8 -> 160 x 160 x 8 0.003 BF
2 conv 8/ 8 3 x 3/ 1 160 x 160 x 8 -> 160 x 160 x 8 0.004 BF
3 conv 4 1 x 1/ 1 160 x 160 x 8 -> 160 x 160 x 4 0.002 BF
4 conv 8 1 x 1/ 1 160 x 160 x 4 -> 160 x 160 x 8 0.002 BF
5 conv 8/ 8 3 x 3/ 1 160 x 160 x 8 -> 160 x 160 x 8 0.004 BF
6 conv 4 1 x 1/ 1 160 x 160 x 8 -> 160 x 160 x 4 0.002 BF
7 dropout p = 0.150 102400 -> 102400
8 Shortcut Layer: 3, wt = 0, wn = 0, outputs: 160 x 160 x 4 0.000 BF
9 conv 24 1 x 1/ 1 160 x 160 x 4 -> 160 x 160 x 24 0.005 BF
10 conv 24/ 24 3 x 3/ 2 160 x 160 x 24 -> 80 x 80 x 24 0.003 BF
11 conv 8 1 x 1/ 1 80 x 80 x 24 -> 80 x 80 x 8 0.002 BF
12 conv 32 1 x 1/ 1 80 x 80 x 8 -> 80 x 80 x 32 0.003 BF
13 conv 32/ 32 3 x 3/ 1 80 x 80 x 32 -> 80 x 80 x 32 0.004 BF
14 conv 8 1 x 1/ 1 80 x 80 x 32 -> 80 x 80 x 8 0.003 BF
15 dropout p = 0.150 51200 -> 51200
16 Shortcut Layer: 11, wt = 0, wn = 0, outputs: 80 x 80 x 8 0.000 BF
17 conv 32 1 x 1/ 1 80 x 80 x 8 -> 80 x 80 x 32 0.003 BF
18 conv 32/ 32 3 x 3/ 1 80 x 80 x 32 -> 80 x 80 x 32 0.004 BF
19 conv 8 1 x 1/ 1 80 x 80 x 32 -> 80 x 80 x 8 0.003 BF
20 dropout p = 0.150 51200 -> 51200
21 Shortcut Layer: 16, wt = 0, wn = 0, outputs: 80 x 80 x 8 0.000 BF
22 conv 32 1 x 1/ 1 80 x 80 x 8 -> 80 x 80 x 32 0.003 BF
23 conv 32/ 32 3 x 3/ 2 80 x 80 x 32 -> 40 x 40 x 32 0.001 BF
24 conv 8 1 x 1/ 1 40 x 40 x 32 -> 40 x 40 x 8 0.001 BF
25 conv 48 1 x 1/ 1 40 x 40 x 8 -> 40 x 40 x 48 0.001 BF
26 conv 48/ 48 3 x 3/ 1 40 x 40 x 48 -> 40 x 40 x 48 0.001 BF
27 conv 8 1 x 1/ 1 40 x 40 x 48 -> 40 x 40 x 8 0.001 BF
28 dropout p = 0.150 12800 -> 12800
29 Shortcut Layer: 24, wt = 0, wn = 0, outputs: 40 x 40 x 8 0.000 BF
30 conv 48 1 x 1/ 1 40 x 40 x 8 -> 40 x 40 x 48 0.001 BF
31 conv 48/ 48 3 x 3/ 1 40 x 40 x 48 -> 40 x 40 x 48 0.001 BF
32 conv 8 1 x 1/ 1 40 x 40 x 48 -> 40 x 40 x 8 0.001 BF
33 dropout p = 0.150 12800 -> 12800
34 Shortcut Layer: 29, wt = 0, wn = 0, outputs: 40 x 40 x 8 0.000 BF
35 conv 48 1 x 1/ 1 40 x 40 x 8 -> 40 x 40 x 48 0.001 BF
36 conv 48/ 48 3 x 3/ 1 40 x 40 x 48 -> 40 x 40 x 48 0.001 BF
37 conv 16 1 x 1/ 1 40 x 40 x 48 -> 40 x 40 x 16 0.002 BF
38 conv 96 1 x 1/ 1 40 x 40 x 16 -> 40 x 40 x 96 0.005 BF
39 conv 96/ 96 3 x 3/ 1 40 x 40 x 96 -> 40 x 40 x 96 0.003 BF
40 conv 16 1 x 1/ 1 40 x 40 x 96 -> 40 x 40 x 16 0.005 BF
41 dropout p = 0.150 25600 -> 25600
42 Shortcut Layer: 37, wt = 0, wn = 0, outputs: 40 x 40 x 16 0.000 BF
43 conv 96 1 x 1/ 1 40 x 40 x 16 -> 40 x 40 x 96 0.005 BF
44 conv 96/ 96 3 x 3/ 1 40 x 40 x 96 -> 40 x 40 x 96 0.003 BF
45 conv 16 1 x 1/ 1 40 x 40 x 96 -> 40 x 40 x 16 0.005 BF
46 dropout p = 0.150 25600 -> 25600
47 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 40 x 40 x 16 0.000 BF
48 conv 96 1 x 1/ 1 40 x 40 x 16 -> 40 x 40 x 96 0.005 BF
49 conv 96/ 96 3 x 3/ 1 40 x 40 x 96 -> 40 x 40 x 96 0.003 BF
50 conv 16 1 x 1/ 1 40 x 40 x 96 -> 40 x 40 x 16 0.005 BF
51 dropout p = 0.150 25600 -> 25600
52 Shortcut Layer: 47, wt = 0, wn = 0, outputs: 40 x 40 x 16 0.000 BF
53 conv 96 1 x 1/ 1 40 x 40 x 16 -> 40 x 40 x 96 0.005 BF
54 conv 96/ 96 3 x 3/ 1 40 x 40 x 96 -> 40 x 40 x 96 0.003 BF
55 conv 16 1 x 1/ 1 40 x 40 x 96 -> 40 x 40 x 16 0.005 BF
56 dropout p = 0.150 25600 -> 25600
57 Shortcut Layer: 52, wt = 0, wn = 0, outputs: 40 x 40 x 16 0.000 BF
58 conv 96 1 x 1/ 1 40 x 40 x 16 -> 40 x 40 x 96 0.005 BF
59 conv 96/ 96 3 x 3/ 2 40 x 40 x 96 -> 20 x 20 x 96 0.001 BF
60 conv 24 1 x 1/ 1 20 x 20 x 96 -> 20 x 20 x 24 0.002 BF
61 conv 136 1 x 1/ 1 20 x 20 x 24 -> 20 x 20 x 136 0.003 BF
62 conv 136/ 136 3 x 3/ 1 20 x 20 x 136 -> 20 x 20 x 136 0.001 BF
63 conv 24 1 x 1/ 1 20 x 20 x 136 -> 20 x 20 x 24 0.003 BF
64 dropout p = 0.150 9600 -> 9600
65 Shortcut Layer: 60, wt = 0, wn = 0, outputs: 20 x 20 x 24 0.000 BF
66 conv 136 1 x 1/ 1 20 x 20 x 24 -> 20 x 20 x 136 0.003 BF
67 conv 136/ 136 3 x 3/ 1 20 x 20 x 136 -> 20 x 20 x 136 0.001 BF
68 conv 24 1 x 1/ 1 20 x 20 x 136 -> 20 x 20 x 24 0.003 BF
69 dropout p = 0.150 9600 -> 9600
70 Shortcut Layer: 65, wt = 0, wn = 0, outputs: 20 x 20 x 24 0.000 BF
71 conv 136 1 x 1/ 1 20 x 20 x 24 -> 20 x 20 x 136 0.003 BF
72 conv 136/ 136 3 x 3/ 1 20 x 20 x 136 -> 20 x 20 x 136 0.001 BF
73 conv 24 1 x 1/ 1 20 x 20 x 136 -> 20 x 20 x 24 0.003 BF
74 dropout p = 0.150 9600 -> 9600
75 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 20 x 20 x 24 0.000 BF
76 conv 136 1 x 1/ 1 20 x 20 x 24 -> 20 x 20 x 136 0.003 BF
77 conv 136/ 136 3 x 3/ 1 20 x 20 x 136 -> 20 x 20 x 136 0.001 BF
78 conv 24 1 x 1/ 1 20 x 20 x 136 -> 20 x 20 x 24 0.003 BF
79 dropout p = 0.150 9600 -> 9600
80 Shortcut Layer: 75, wt = 0, wn = 0, outputs: 20 x 20 x 24 0.000 BF
81 conv 136 1 x 1/ 1 20 x 20 x 24 -> 20 x 20 x 136 0.003 BF
82 conv 136/ 136 3 x 3/ 2 20 x 20 x 136 -> 10 x 10 x 136 0.000 BF
83 conv 48 1 x 1/ 1 10 x 10 x 136 -> 10 x 10 x 48 0.001 BF
84 conv 224 1 x 1/ 1 10 x 10 x 48 -> 10 x 10 x 224 0.002 BF
85 conv 224/ 224 3 x 3/ 1 10 x 10 x 224 -> 10 x 10 x 224 0.000 BF
86 conv 48 1 x 1/ 1 10 x 10 x 224 -> 10 x 10 x 48 0.002 BF
87 dropout p = 0.150 4800 -> 4800
88 Shortcut Layer: 83, wt = 0, wn = 0, outputs: 10 x 10 x 48 0.000 BF
89 conv 224 1 x 1/ 1 10 x 10 x 48 -> 10 x 10 x 224 0.002 BF
90 conv 224/ 224 3 x 3/ 1 10 x 10 x 224 -> 10 x 10 x 224 0.000 BF
91 conv 48 1 x 1/ 1 10 x 10 x 224 -> 10 x 10 x 48 0.002 BF
92 dropout p = 0.150 4800 -> 4800
93 Shortcut Layer: 88, wt = 0, wn = 0, outputs: 10 x 10 x 48 0.000 BF
94 conv 224 1 x 1/ 1 10 x 10 x 48 -> 10 x 10 x 224 0.002 BF
95 conv 224/ 224 3 x 3/ 1 10 x 10 x 224 -> 10 x 10 x 224 0.000 BF
96 conv 48 1 x 1/ 1 10 x 10 x 224 -> 10 x 10 x 48 0.002 BF
97 dropout p = 0.150 4800 -> 4800
98 Shortcut Layer: 93, wt = 0, wn = 0, outputs: 10 x 10 x 48 0.000 BF
99 conv 224 1 x 1/ 1 10 x 10 x 48 -> 10 x 10 x 224 0.002 BF
100 conv 224/ 224 3 x 3/ 1 10 x 10 x 224 -> 10 x 10 x 224 0.000 BF
101 conv 48 1 x 1/ 1 10 x 10 x 224 -> 10 x 10 x 48 0.002 BF
102 dropout p = 0.150 4800 -> 4800
103 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 10 x 10 x 48 0.000 BF
104 conv 224 1 x 1/ 1 10 x 10 x 48 -> 10 x 10 x 224 0.002 BF
105 conv 224/ 224 3 x 3/ 1 10 x 10 x 224 -> 10 x 10 x 224 0.000 BF
106 conv 48 1 x 1/ 1 10 x 10 x 224 -> 10 x 10 x 48 0.002 BF
107 dropout p = 0.150 4800 -> 4800
108 Shortcut Layer: 103, wt = 0, wn = 0, outputs: 10 x 10 x 48 0.000 BF
109 max 3x 3/ 1 10 x 10 x 48 -> 10 x 10 x 48 0.000 BF
110 route 108 -> 10 x 10 x 48
111 max 5x 5/ 1 10 x 10 x 48 -> 10 x 10 x 48 0.000 BF
112 route 108 -> 10 x 10 x 48
113 max 9x 9/ 1 10 x 10 x 48 -> 10 x 10 x 48 0.000 BF
114 route 113 111 109 108 -> 10 x 10 x 192
115 conv 96 1 x 1/ 1 10 x 10 x 192 -> 10 x 10 x 96 0.004 BF
116 conv 96/ 96 5 x 5/ 1 10 x 10 x 96 -> 10 x 10 x 96 0.000 BF
117 conv 96 1 x 1/ 1 10 x 10 x 96 -> 10 x 10 x 96 0.002 BF
118 conv 96/ 96 5 x 5/ 1 10 x 10 x 96 -> 10 x 10 x 96 0.000 BF
119 conv 96 1 x 1/ 1 10 x 10 x 96 -> 10 x 10 x 96 0.002 BF
120 conv 255 1 x 1/ 1 10 x 10 x 96 -> 10 x 10 x 255 0.005 BF
121 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: 1.00
nms_kind: greedynms (1), beta = 0.600000
122 route 115 -> 10 x 10 x 96
123 upsample 2x 10 x 10 x 96 -> 20 x 20 x 96
124 route 123 80 -> 20 x 20 x 120
125 conv 120/ 120 5 x 5/ 1 20 x 20 x 120 -> 20 x 20 x 120 0.002 BF
126 conv 120 1 x 1/ 1 20 x 20 x 120 -> 20 x 20 x 120 0.012 BF
127 conv 120/ 120 5 x 5/ 1 20 x 20 x 120 -> 20 x 20 x 120 0.002 BF
128 conv 120 1 x 1/ 1 20 x 20 x 120 -> 20 x 20 x 120 0.012 BF
129 conv 255 1 x 1/ 1 20 x 20 x 120 -> 20 x 20 x 255 0.024 BF
130 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: 1.00
nms_kind: greedynms (1), beta = 0.600000
Total BFLOPS 0.252
avg_outputs = 62893
Allocate additional workspace_size = 1.23 MB
Loading weights from yolo-fastest-1.1.weights...
seen 64, trained: 14231 K-images (222 Kilo-batches_64)
Done! Loaded 131 layers from weights-file
calculation mAP (mean average precision)...
Detection layer: 121 - type = 28
Detection layer: 130 - type = 28
4952
detections_count = 897029, unique_truth_count = 36335
class_id = 0, name = person, ap = 45.27% (TP = 4021, FP = 6119)
class_id = 1, name = bicycle, ap = 16.88% (TP = 43, FP = 72)
class_id = 2, name = car, ap = 20.98% (TP = 484, FP = 1112)
class_id = 3, name = motorcycle, ap = 36.12% (TP = 129, FP = 160)
class_id = 4, name = airplane, ap = 57.68% (TP = 81, FP = 57)
class_id = 5, name = bus, ap = 52.42% (TP = 125, FP = 80)
class_id = 6, name = train, ap = 63.20% (TP = 110, FP = 60)
class_id = 7, name = truck, ap = 18.15% (TP = 70, FP = 104)
class_id = 8, name = boat, ap = 12.82% (TP = 70, FP = 188)
class_id = 9, name = traffic light, ap = 9.76% (TP = 76, FP = 162)
class_id = 10, name = fire hydrant, ap = 49.26% (TP = 46, FP = 40)
class_id = 11, name = stop sign, ap = 51.04% (TP = 39, FP = 21)
class_id = 12, name = parking meter, ap = 25.85% (TP = 13, FP = 5)
class_id = 13, name = bench, ap = 12.02% (TP = 43, FP = 55)
class_id = 14, name = bird, ap = 14.24% (TP = 64, FP = 137)
class_id = 15, name = cat, ap = 59.32% (TP = 98, FP = 126)
class_id = 16, name = dog, ap = 41.95% (TP = 80, FP = 95)
class_id = 17, name = horse, ap = 43.46% (TP = 120, FP = 151)
class_id = 18, name = sheep, ap = 33.25% (TP = 147, FP = 285)
class_id = 19, name = cow, ap = 35.18% (TP = 146, FP = 205)
class_id = 20, name = elephant, ap = 59.49% (TP = 151, FP = 152)
class_id = 21, name = bear, ap = 58.50% (TP = 46, FP = 44)
class_id = 22, name = zebra, ap = 66.36% (TP = 172, FP = 123)
class_id = 23, name = giraffe, ap = 65.48% (TP = 150, FP = 63)
class_id = 24, name = backpack, ap = 1.91% (TP = 4, FP = 22)
class_id = 25, name = umbrella, ap = 21.44% (TP = 91, FP = 138)
class_id = 26, name = handbag, ap = 0.61% (TP = 1, FP = 23)
class_id = 27, name = tie, ap = 10.44% (TP = 31, FP = 94)
class_id = 28, name = suitcase, ap = 12.93% (TP = 39, FP = 78)
class_id = 29, name = frisbee, ap = 27.25% (TP = 28, FP = 41)
class_id = 30, name = skis, ap = 11.67% (TP = 37, FP = 132)
class_id = 31, name = snowboard, ap = 10.36% (TP = 6, FP = 10)
class_id = 32, name = sports ball, ap = 17.34% (TP = 48, FP = 62)
class_id = 33, name = kite, ap = 25.58% (TP = 117, FP = 232)
class_id = 34, name = baseball bat, ap = 11.47% (TP = 15, FP = 27)
class_id = 35, name = baseball glove, ap = 10.58% (TP = 20, FP = 61)
class_id = 36, name = skateboard, ap = 18.58% (TP = 44, FP = 85)
class_id = 37, name = surfboard, ap = 14.43% (TP = 50, FP = 172)
class_id = 38, name = tennis racket, ap = 22.89% (TP = 67, FP = 116)
class_id = 39, name = bottle, ap = 7.63% (TP = 69, FP = 146)
class_id = 40, name = wine glass, ap = 7.97% (TP = 18, FP = 67)
class_id = 41, name = cup, ap = 13.11% (TP = 116, FP = 243)
class_id = 42, name = fork, ap = 4.41% (TP = 9, FP = 13)
class_id = 43, name = knife, ap = 1.48% (TP = 2, FP = 14)
class_id = 44, name = spoon, ap = 0.77% (TP = 1, FP = 6)
class_id = 45, name = bowl, ap = 23.25% (TP = 134, FP = 241)
class_id = 46, name = banana, ap = 8.99% (TP = 39, FP = 105)
class_id = 47, name = apple, ap = 5.32% (TP = 13, FP = 37)
class_id = 48, name = sandwich, ap = 23.40% (TP = 35, FP = 67)
class_id = 49, name = orange, ap = 16.69% (TP = 52, FP = 91)
class_id = 50, name = broccoli, ap = 16.88% (TP = 65, FP = 164)
class_id = 51, name = carrot, ap = 7.64% (TP = 27, FP = 80)
class_id = 52, name = hot dog, ap = 14.46% (TP = 11, FP = 31)
class_id = 53, name = pizza, ap = 41.55% (TP = 113, FP = 124)
class_id = 54, name = donut, ap = 19.84% (TP = 65, FP = 152)
class_id = 55, name = cake, ap = 18.44% (TP = 45, FP = 72)
class_id = 56, name = chair, ap = 10.04% (TP = 142, FP = 275)
class_id = 57, name = couch, ap = 29.89% (TP = 53, FP = 101)
class_id = 58, name = potted plant, ap = 10.76% (TP = 29, FP = 84)
class_id = 59, name = bed, ap = 43.32% (TP = 57, FP = 71)
class_id = 60, name = dining table, ap = 22.00% (TP = 183, FP = 283)
class_id = 61, name = toilet, ap = 58.93% (TP = 94, FP = 89)
class_id = 62, name = tv, ap = 47.13% (TP = 123, FP = 107)
class_id = 63, name = laptop, ap = 40.93% (TP = 75, FP = 112)
class_id = 64, name = mouse, ap = 32.37% (TP = 29, FP = 26)
class_id = 65, name = remote, ap = 4.22% (TP = 12, FP = 19)
class_id = 66, name = keyboard, ap = 31.90% (TP = 51, FP = 67)
class_id = 67, name = cell phone, ap = 15.28% (TP = 30, FP = 30)
class_id = 68, name = microwave, ap = 39.49% (TP = 20, FP = 14)
class_id = 69, name = oven, ap = 24.75% (TP = 34, FP = 45)
class_id = 70, name = toaster, ap = 2.32% (TP = 0, FP = 0)
class_id = 71, name = sink, ap = 20.24% (TP = 46, FP = 86)
class_id = 72, name = refrigerator, ap = 30.95% (TP = 42, FP = 44)
class_id = 73, name = book, ap = 1.74% (TP = 45, FP = 334)
class_id = 74, name = clock, ap = 32.38% (TP = 103, FP = 127)
class_id = 75, name = vase, ap = 13.89% (TP = 40, FP = 48)
class_id = 76, name = scissors, ap = 6.25% (TP = 1, FP = 3)
class_id = 77, name = teddy bear, ap = 33.81% (TP = 59, FP = 56)
class_id = 78, name = hair drier, ap = 0.00% (TP = 0, FP = 0)
class_id = 79, name = toothbrush, ap = 1.16% (TP = 0, FP = 2)
for conf_thresh = 0.25, precision = 0.39, recall = 0.25, F1-score = 0.31
for conf_thresh = 0.25, TP = 9204, FP = 14585, FN = 27131, average IoU = 27.42 %
IoU threshold = 50 %, used Area-Under-Curve for each unique Recall
mean average precision (mAP@0.50) = 0.243967, or 24.40 %
Total Detection Time: 133 Seconds

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samples/sample_c++/module_sample/liveview/data/yolo-fastest-1.1_coco/xl-mAP_log.txt Normal file
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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 16 3 x 3/ 2 320 x 320 x 3 -> 160 x 160 x 16 0.022 BF
1 conv 16 1 x 1/ 1 160 x 160 x 16 -> 160 x 160 x 16 0.013 BF
2 conv 16/ 16 3 x 3/ 1 160 x 160 x 16 -> 160 x 160 x 16 0.007 BF
3 conv 8 1 x 1/ 1 160 x 160 x 16 -> 160 x 160 x 8 0.007 BF
4 conv 16 1 x 1/ 1 160 x 160 x 8 -> 160 x 160 x 16 0.007 BF
5 conv 16/ 16 3 x 3/ 1 160 x 160 x 16 -> 160 x 160 x 16 0.007 BF
6 conv 8 1 x 1/ 1 160 x 160 x 16 -> 160 x 160 x 8 0.007 BF
7 dropout p = 0.200 204800 -> 204800
8 Shortcut Layer: 3, wt = 0, wn = 0, outputs: 160 x 160 x 8 0.000 BF
9 conv 48 1 x 1/ 1 160 x 160 x 8 -> 160 x 160 x 48 0.020 BF
10 conv 48/ 48 3 x 3/ 2 160 x 160 x 48 -> 80 x 80 x 48 0.006 BF
11 conv 16 1 x 1/ 1 80 x 80 x 48 -> 80 x 80 x 16 0.010 BF
12 conv 64 1 x 1/ 1 80 x 80 x 16 -> 80 x 80 x 64 0.013 BF
13 conv 64/ 64 3 x 3/ 1 80 x 80 x 64 -> 80 x 80 x 64 0.007 BF
14 conv 16 1 x 1/ 1 80 x 80 x 64 -> 80 x 80 x 16 0.013 BF
15 dropout p = 0.200 102400 -> 102400
16 Shortcut Layer: 11, wt = 0, wn = 0, outputs: 80 x 80 x 16 0.000 BF
17 conv 64 1 x 1/ 1 80 x 80 x 16 -> 80 x 80 x 64 0.013 BF
18 conv 64/ 64 3 x 3/ 1 80 x 80 x 64 -> 80 x 80 x 64 0.007 BF
19 conv 16 1 x 1/ 1 80 x 80 x 64 -> 80 x 80 x 16 0.013 BF
20 dropout p = 0.200 102400 -> 102400
21 Shortcut Layer: 16, wt = 0, wn = 0, outputs: 80 x 80 x 16 0.000 BF
22 conv 64 1 x 1/ 1 80 x 80 x 16 -> 80 x 80 x 64 0.013 BF
23 conv 64/ 64 3 x 3/ 2 80 x 80 x 64 -> 40 x 40 x 64 0.002 BF
24 conv 16 1 x 1/ 1 40 x 40 x 64 -> 40 x 40 x 16 0.003 BF
25 conv 96 1 x 1/ 1 40 x 40 x 16 -> 40 x 40 x 96 0.005 BF
26 conv 96/ 96 3 x 3/ 1 40 x 40 x 96 -> 40 x 40 x 96 0.003 BF
27 conv 16 1 x 1/ 1 40 x 40 x 96 -> 40 x 40 x 16 0.005 BF
28 dropout p = 0.200 25600 -> 25600
29 Shortcut Layer: 24, wt = 0, wn = 0, outputs: 40 x 40 x 16 0.000 BF
30 conv 96 1 x 1/ 1 40 x 40 x 16 -> 40 x 40 x 96 0.005 BF
31 conv 96/ 96 3 x 3/ 1 40 x 40 x 96 -> 40 x 40 x 96 0.003 BF
32 conv 16 1 x 1/ 1 40 x 40 x 96 -> 40 x 40 x 16 0.005 BF
33 dropout p = 0.200 25600 -> 25600
34 Shortcut Layer: 29, wt = 0, wn = 0, outputs: 40 x 40 x 16 0.000 BF
35 conv 96 1 x 1/ 1 40 x 40 x 16 -> 40 x 40 x 96 0.005 BF
36 conv 96/ 96 3 x 3/ 1 40 x 40 x 96 -> 40 x 40 x 96 0.003 BF
37 conv 32 1 x 1/ 1 40 x 40 x 96 -> 40 x 40 x 32 0.010 BF
38 conv 192 1 x 1/ 1 40 x 40 x 32 -> 40 x 40 x 192 0.020 BF
39 conv 192/ 192 3 x 3/ 1 40 x 40 x 192 -> 40 x 40 x 192 0.006 BF
40 conv 32 1 x 1/ 1 40 x 40 x 192 -> 40 x 40 x 32 0.020 BF
41 dropout p = 0.200 51200 -> 51200
42 Shortcut Layer: 37, wt = 0, wn = 0, outputs: 40 x 40 x 32 0.000 BF
43 conv 192 1 x 1/ 1 40 x 40 x 32 -> 40 x 40 x 192 0.020 BF
44 conv 192/ 192 3 x 3/ 1 40 x 40 x 192 -> 40 x 40 x 192 0.006 BF
45 conv 32 1 x 1/ 1 40 x 40 x 192 -> 40 x 40 x 32 0.020 BF
46 dropout p = 0.200 51200 -> 51200
47 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 40 x 40 x 32 0.000 BF
48 conv 192 1 x 1/ 1 40 x 40 x 32 -> 40 x 40 x 192 0.020 BF
49 conv 192/ 192 3 x 3/ 1 40 x 40 x 192 -> 40 x 40 x 192 0.006 BF
50 conv 32 1 x 1/ 1 40 x 40 x 192 -> 40 x 40 x 32 0.020 BF
51 dropout p = 0.200 51200 -> 51200
52 Shortcut Layer: 47, wt = 0, wn = 0, outputs: 40 x 40 x 32 0.000 BF
53 conv 192 1 x 1/ 1 40 x 40 x 32 -> 40 x 40 x 192 0.020 BF
54 conv 192/ 192 3 x 3/ 1 40 x 40 x 192 -> 40 x 40 x 192 0.006 BF
55 conv 32 1 x 1/ 1 40 x 40 x 192 -> 40 x 40 x 32 0.020 BF
56 dropout p = 0.200 51200 -> 51200
57 Shortcut Layer: 52, wt = 0, wn = 0, outputs: 40 x 40 x 32 0.000 BF
58 conv 192 1 x 1/ 1 40 x 40 x 32 -> 40 x 40 x 192 0.020 BF
59 conv 192/ 192 3 x 3/ 2 40 x 40 x 192 -> 20 x 20 x 192 0.001 BF
60 conv 48 1 x 1/ 1 20 x 20 x 192 -> 20 x 20 x 48 0.007 BF
61 conv 272 1 x 1/ 1 20 x 20 x 48 -> 20 x 20 x 272 0.010 BF
62 conv 272/ 272 3 x 3/ 1 20 x 20 x 272 -> 20 x 20 x 272 0.002 BF
63 conv 48 1 x 1/ 1 20 x 20 x 272 -> 20 x 20 x 48 0.010 BF
64 dropout p = 0.200 19200 -> 19200
65 Shortcut Layer: 60, wt = 0, wn = 0, outputs: 20 x 20 x 48 0.000 BF
66 conv 272 1 x 1/ 1 20 x 20 x 48 -> 20 x 20 x 272 0.010 BF
67 conv 272/ 272 3 x 3/ 1 20 x 20 x 272 -> 20 x 20 x 272 0.002 BF
68 conv 48 1 x 1/ 1 20 x 20 x 272 -> 20 x 20 x 48 0.010 BF
69 dropout p = 0.200 19200 -> 19200
70 Shortcut Layer: 65, wt = 0, wn = 0, outputs: 20 x 20 x 48 0.000 BF
71 conv 272 1 x 1/ 1 20 x 20 x 48 -> 20 x 20 x 272 0.010 BF
72 conv 272/ 272 3 x 3/ 1 20 x 20 x 272 -> 20 x 20 x 272 0.002 BF
73 conv 48 1 x 1/ 1 20 x 20 x 272 -> 20 x 20 x 48 0.010 BF
74 dropout p = 0.200 19200 -> 19200
75 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 20 x 20 x 48 0.000 BF
76 conv 272 1 x 1/ 1 20 x 20 x 48 -> 20 x 20 x 272 0.010 BF
77 conv 272/ 272 3 x 3/ 1 20 x 20 x 272 -> 20 x 20 x 272 0.002 BF
78 conv 48 1 x 1/ 1 20 x 20 x 272 -> 20 x 20 x 48 0.010 BF
79 dropout p = 0.200 19200 -> 19200
80 Shortcut Layer: 75, wt = 0, wn = 0, outputs: 20 x 20 x 48 0.000 BF
81 conv 272 1 x 1/ 1 20 x 20 x 48 -> 20 x 20 x 272 0.010 BF
82 conv 272/ 272 3 x 3/ 2 20 x 20 x 272 -> 10 x 10 x 272 0.000 BF
83 conv 96 1 x 1/ 1 10 x 10 x 272 -> 10 x 10 x 96 0.005 BF
84 conv 448 1 x 1/ 1 10 x 10 x 96 -> 10 x 10 x 448 0.009 BF
85 conv 448/ 448 3 x 3/ 1 10 x 10 x 448 -> 10 x 10 x 448 0.001 BF
86 conv 96 1 x 1/ 1 10 x 10 x 448 -> 10 x 10 x 96 0.009 BF
87 dropout p = 0.200 9600 -> 9600
88 Shortcut Layer: 83, wt = 0, wn = 0, outputs: 10 x 10 x 96 0.000 BF
89 conv 448 1 x 1/ 1 10 x 10 x 96 -> 10 x 10 x 448 0.009 BF
90 conv 448/ 448 3 x 3/ 1 10 x 10 x 448 -> 10 x 10 x 448 0.001 BF
91 conv 96 1 x 1/ 1 10 x 10 x 448 -> 10 x 10 x 96 0.009 BF
92 dropout p = 0.200 9600 -> 9600
93 Shortcut Layer: 88, wt = 0, wn = 0, outputs: 10 x 10 x 96 0.000 BF
94 conv 448 1 x 1/ 1 10 x 10 x 96 -> 10 x 10 x 448 0.009 BF
95 conv 448/ 448 3 x 3/ 1 10 x 10 x 448 -> 10 x 10 x 448 0.001 BF
96 conv 96 1 x 1/ 1 10 x 10 x 448 -> 10 x 10 x 96 0.009 BF
97 dropout p = 0.200 9600 -> 9600
98 Shortcut Layer: 93, wt = 0, wn = 0, outputs: 10 x 10 x 96 0.000 BF
99 conv 448 1 x 1/ 1 10 x 10 x 96 -> 10 x 10 x 448 0.009 BF
100 conv 448/ 448 3 x 3/ 1 10 x 10 x 448 -> 10 x 10 x 448 0.001 BF
101 conv 96 1 x 1/ 1 10 x 10 x 448 -> 10 x 10 x 96 0.009 BF
102 dropout p = 0.200 9600 -> 9600
103 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 10 x 10 x 96 0.000 BF
104 conv 448 1 x 1/ 1 10 x 10 x 96 -> 10 x 10 x 448 0.009 BF
105 conv 448/ 448 3 x 3/ 1 10 x 10 x 448 -> 10 x 10 x 448 0.001 BF
106 conv 96 1 x 1/ 1 10 x 10 x 448 -> 10 x 10 x 96 0.009 BF
107 dropout p = 0.200 9600 -> 9600
108 Shortcut Layer: 103, wt = 0, wn = 0, outputs: 10 x 10 x 96 0.000 BF
109 max 3x 3/ 1 10 x 10 x 96 -> 10 x 10 x 96 0.000 BF
110 route 108 -> 10 x 10 x 96
111 max 5x 5/ 1 10 x 10 x 96 -> 10 x 10 x 96 0.000 BF
112 route 108 -> 10 x 10 x 96
113 max 9x 9/ 1 10 x 10 x 96 -> 10 x 10 x 96 0.001 BF
114 route 113 111 109 108 -> 10 x 10 x 384
115 conv 96 1 x 1/ 1 10 x 10 x 384 -> 10 x 10 x 96 0.007 BF
116 conv 96/ 96 5 x 5/ 1 10 x 10 x 96 -> 10 x 10 x 96 0.000 BF
117 conv 96 1 x 1/ 1 10 x 10 x 96 -> 10 x 10 x 96 0.002 BF
118 conv 96/ 96 5 x 5/ 1 10 x 10 x 96 -> 10 x 10 x 96 0.000 BF
119 conv 96 1 x 1/ 1 10 x 10 x 96 -> 10 x 10 x 96 0.002 BF
120 conv 255 1 x 1/ 1 10 x 10 x 96 -> 10 x 10 x 255 0.005 BF
121 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: 1.00
nms_kind: greedynms (1), beta = 0.600000
122 route 115 -> 10 x 10 x 96
123 upsample 2x 10 x 10 x 96 -> 20 x 20 x 96
124 route 123 80 -> 20 x 20 x 144
125 conv 144/ 144 5 x 5/ 1 20 x 20 x 144 -> 20 x 20 x 144 0.003 BF
126 conv 144 1 x 1/ 1 20 x 20 x 144 -> 20 x 20 x 144 0.017 BF
127 conv 144/ 144 5 x 5/ 1 20 x 20 x 144 -> 20 x 20 x 144 0.003 BF
128 conv 144 1 x 1/ 1 20 x 20 x 144 -> 20 x 20 x 144 0.017 BF
129 conv 255 1 x 1/ 1 20 x 20 x 144 -> 20 x 20 x 255 0.029 BF
130 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: 1.00
nms_kind: greedynms (1), beta = 0.600000
Total BFLOPS 0.725
avg_outputs = 120982
Allocate additional workspace_size = 0.31 MB
Loading weights from model/yolo-fastest-1_final.weights...
seen 64, trained: 16000 K-images (250 Kilo-batches_64)
Done! Loaded 131 layers from weights-file
calculation mAP (mean average precision)...
Detection layer: 121 - type = 28
Detection layer: 130 - type = 28
4952
detections_count = 664785, unique_truth_count = 36335
class_id = 0, name = person, ap = 53.92% (TP = 4976, FP = 5767)
class_id = 1, name = bicycle, ap = 25.29% (TP = 81, FP = 105)
class_id = 2, name = car, ap = 30.59% (TP = 666, FP = 1092)
class_id = 3, name = motorcycle, ap = 47.05% (TP = 157, FP = 174)
class_id = 4, name = airplane, ap = 63.87% (TP = 87, FP = 63)
class_id = 5, name = bus, ap = 60.84% (TP = 160, FP = 90)
class_id = 6, name = train, ap = 72.50% (TP = 124, FP = 59)
class_id = 7, name = truck, ap = 30.67% (TP = 126, FP = 177)
class_id = 8, name = boat, ap = 20.35% (TP = 111, FP = 233)
class_id = 9, name = traffic light, ap = 17.36% (TP = 147, FP = 311)
class_id = 10, name = fire hydrant, ap = 63.01% (TP = 54, FP = 22)
class_id = 11, name = stop sign, ap = 54.51% (TP = 38, FP = 25)
class_id = 12, name = parking meter, ap = 39.62% (TP = 24, FP = 12)
class_id = 13, name = bench, ap = 16.95% (TP = 67, FP = 120)
class_id = 14, name = bird, ap = 22.58% (TP = 104, FP = 185)
class_id = 15, name = cat, ap = 73.95% (TP = 129, FP = 112)
class_id = 16, name = dog, ap = 58.90% (TP = 118, FP = 128)
class_id = 17, name = horse, ap = 57.27% (TP = 153, FP = 120)
class_id = 18, name = sheep, ap = 45.20% (TP = 185, FP = 305)
class_id = 19, name = cow, ap = 48.22% (TP = 191, FP = 212)
class_id = 20, name = elephant, ap = 68.17% (TP = 176, FP = 147)
class_id = 21, name = bear, ap = 77.67% (TP = 51, FP = 28)
class_id = 22, name = zebra, ap = 74.43% (TP = 183, FP = 91)
class_id = 23, name = giraffe, ap = 75.02% (TP = 166, FP = 65)
class_id = 24, name = backpack, ap = 5.03% (TP = 21, FP = 86)
class_id = 25, name = umbrella, ap = 36.33% (TP = 151, FP = 161)
class_id = 26, name = handbag, ap = 1.68% (TP = 11, FP = 72)
class_id = 27, name = tie, ap = 20.32% (TP = 60, FP = 120)
class_id = 28, name = suitcase, ap = 21.99% (TP = 73, FP = 137)
class_id = 29, name = frisbee, ap = 46.40% (TP = 57, FP = 60)
class_id = 30, name = skis, ap = 19.74% (TP = 60, FP = 153)
class_id = 31, name = snowboard, ap = 18.86% (TP = 20, FP = 51)
class_id = 32, name = sports ball, ap = 28.16% (TP = 74, FP = 72)
class_id = 33, name = kite, ap = 35.39% (TP = 139, FP = 247)
class_id = 34, name = baseball bat, ap = 20.85% (TP = 33, FP = 63)
class_id = 35, name = baseball glove, ap = 21.76% (TP = 40, FP = 97)
class_id = 36, name = skateboard, ap = 36.03% (TP = 79, FP = 112)
class_id = 37, name = surfboard, ap = 27.98% (TP = 93, FP = 194)
class_id = 38, name = tennis racket, ap = 36.49% (TP = 99, FP = 175)
class_id = 39, name = bottle, ap = 16.24% (TP = 170, FP = 327)
class_id = 40, name = wine glass, ap = 15.37% (TP = 48, FP = 125)
class_id = 41, name = cup, ap = 23.22% (TP = 211, FP = 348)
class_id = 42, name = fork, ap = 14.48% (TP = 29, FP = 60)
class_id = 43, name = knife, ap = 4.63% (TP = 15, FP = 62)
class_id = 44, name = spoon, ap = 3.32% (TP = 9, FP = 27)
class_id = 45, name = bowl, ap = 33.69% (TP = 209, FP = 261)
class_id = 46, name = banana, ap = 23.40% (TP = 86, FP = 136)
class_id = 47, name = apple, ap = 8.21% (TP = 24, FP = 89)
class_id = 48, name = sandwich, ap = 33.67% (TP = 56, FP = 80)
class_id = 49, name = orange, ap = 22.59% (TP = 77, FP = 137)
class_id = 50, name = broccoli, ap = 23.62% (TP = 88, FP = 178)
class_id = 51, name = carrot, ap = 10.15% (TP = 55, FP = 159)
class_id = 52, name = hot dog, ap = 28.57% (TP = 33, FP = 38)
class_id = 53, name = pizza, ap = 51.21% (TP = 129, FP = 148)
class_id = 54, name = donut, ap = 30.97% (TP = 116, FP = 184)
class_id = 55, name = cake, ap = 32.03% (TP = 99, FP = 155)
class_id = 56, name = chair, ap = 18.50% (TP = 304, FP = 568)
class_id = 57, name = couch, ap = 48.84% (TP = 125, FP = 156)
class_id = 58, name = potted plant, ap = 20.71% (TP = 66, FP = 118)
class_id = 59, name = bed, ap = 52.73% (TP = 88, FP = 97)
class_id = 60, name = dining table, ap = 27.14% (TP = 224, FP = 334)
class_id = 61, name = toilet, ap = 66.39% (TP = 112, FP = 77)
class_id = 62, name = tv, ap = 56.32% (TP = 151, FP = 98)
class_id = 63, name = laptop, ap = 54.05% (TP = 100, FP = 157)
class_id = 64, name = mouse, ap = 44.78% (TP = 46, FP = 44)
class_id = 65, name = remote, ap = 7.84% (TP = 28, FP = 102)
class_id = 66, name = keyboard, ap = 44.37% (TP = 71, FP = 83)
class_id = 67, name = cell phone, ap = 24.25% (TP = 62, FP = 74)
class_id = 68, name = microwave, ap = 46.90% (TP = 21, FP = 19)
class_id = 69, name = oven, ap = 37.19% (TP = 54, FP = 52)
class_id = 70, name = toaster, ap = 10.84% (TP = 0, FP = 0)
class_id = 71, name = sink, ap = 34.06% (TP = 81, FP = 98)
class_id = 72, name = refrigerator, ap = 46.76% (TP = 57, FP = 45)
class_id = 73, name = book, ap = 4.20% (TP = 112, FP = 548)
class_id = 74, name = clock, ap = 53.92% (TP = 144, FP = 92)
class_id = 75, name = vase, ap = 25.27% (TP = 67, FP = 70)
class_id = 76, name = scissors, ap = 21.61% (TP = 7, FP = 10)
class_id = 77, name = teddy bear, ap = 47.50% (TP = 90, FP = 56)
class_id = 78, name = hair drier, ap = 0.70% (TP = 0, FP = 0)
class_id = 79, name = toothbrush, ap = 1.50% (TP = 2, FP = 9)
for conf_thresh = 0.25, precision = 0.43, recall = 0.35, F1-score = 0.39
for conf_thresh = 0.25, TP = 12750, FP = 16864, FN = 23585, average IoU = 31.39 %
IoU threshold = 50 %, used Area-Under-Curve for each unique Recall
mean average precision (mAP@0.50) = 0.343340, or 34.33 %
Total Detection Time: 93 Seconds

947
samples/sample_c++/module_sample/liveview/data/yolo-fastest-1.1_coco/yolo-fastest-1.1-xl.cfg Normal file
View File

@ -0,0 +1,947 @@
[net]
batch=32
subdivisions=1
width=320
height=320
channels=3
momentum=0.949
decay=0.0005
angle=0
saturation=1.5
exposure=1.5
hue=.1
learning_rate=0.001
burn_in=4000
max_batches=500000
policy=steps
steps=400000,450000
scales=.1,.1
[convolutional]
filters=16
size=3
pad=1
stride=2
batch_normalize=1
activation=leaky
[convolutional]
filters=16
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=16
filters=16
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=8
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[convolutional]
filters=16
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=16
filters=16
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=8
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.2
[shortcut]
from=-5
activation=linear
[convolutional]
filters=48
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=48
filters=48
size=3
pad=1
stride=2
batch_normalize=1
activation=leaky
[convolutional]
filters=16
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[convolutional]
filters=64
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=64
filters=64
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=16
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.2
[shortcut]
from=-5
activation=linear
[convolutional]
filters=64
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=64
filters=64
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=16
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.2
[shortcut]
from=-5
activation=linear
[convolutional]
filters=64
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=64
filters=64
size=3
pad=1
stride=2
batch_normalize=1
activation=leaky
[convolutional]
filters=16
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[convolutional]
filters=96
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=96
filters=96
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=16
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.2
[shortcut]
from=-5
activation=linear
[convolutional]
filters=96
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=96
filters=96
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=16
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.2
[shortcut]
from=-5
activation=linear
[convolutional]
filters=96
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=96
filters=96
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=32
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[convolutional]
filters=192
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=192
filters=192
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=32
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.2
[shortcut]
from=-5
activation=linear
[convolutional]
filters=192
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=192
filters=192
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=32
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.2
[shortcut]
from=-5
activation=linear
[convolutional]
filters=192
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=192
filters=192
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=32
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.2
[shortcut]
from=-5
activation=linear
[convolutional]
filters=192
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=192
filters=192
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=32
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.2
[shortcut]
from=-5
activation=linear
[convolutional]
filters=192
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=192
filters=192
size=3
pad=1
stride=2
batch_normalize=1
activation=leaky
[convolutional]
filters=48
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[convolutional]
filters=272
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=272
filters=272
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=48
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.2
[shortcut]
from=-5
activation=linear
[convolutional]
filters=272
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=272
filters=272
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=48
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.2
[shortcut]
from=-5
activation=linear
[convolutional]
filters=272
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=272
filters=272
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=48
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.2
[shortcut]
from=-5
activation=linear
[convolutional]
filters=272
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=272
filters=272
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=48
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.2
[shortcut]
from=-5
activation=linear
[convolutional]
filters=272
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=272
filters=272
size=3
pad=1
stride=2
batch_normalize=1
activation=leaky
[convolutional]
filters=96
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[convolutional]
filters=448
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=448
filters=448
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=96
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.2
[shortcut]
from=-5
activation=linear
[convolutional]
filters=448
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=448
filters=448
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=96
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.2
[shortcut]
from=-5
activation=linear
[convolutional]
filters=448
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=448
filters=448
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=96
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.2
[shortcut]
from=-5
activation=linear
[convolutional]
filters=448
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=448
filters=448
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=96
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.2
[shortcut]
from=-5
activation=linear
[convolutional]
filters=448
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=448
filters=448
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=96
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.2
[shortcut]
from=-5
activation=linear
###############
[maxpool]
stride=1
size=3
[route]
layers=-2
[maxpool]
stride=1
size=5
[route]
layers=-4
[maxpool]
stride=1
size=9
[route]
layers=-1,-3,-5,-6
### End SPP ###
###############
[convolutional]
filters=96
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
filters=96
size=5
groups=96
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=96
size=1
stride=1
pad=1
batch_normalize=1
activation=linear
[convolutional]
filters=96
size=5
groups=96
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=96
size=1
stride=1
pad=1
batch_normalize=1
activation=linear
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 3,4,5
anchors = 12, 18, 37, 49, 52,132, 115, 73, 119,199, 242,238
classes=80
num=6
jitter=.15
ignore_thresh = .5
truth_thresh = 1
random=0
scale_x_y = 1.0
iou_thresh=0.213
cls_normalizer=1.0
iou_normalizer=0.07
iou_loss=ciou
nms_kind=greedynms
beta_nms=0.6
[route]
layers = -7
[upsample]
stride = 2
[route]
layers=-1,80
[convolutional]
filters=144
size=5
groups=144
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=144
size=1
stride=1
pad=1
batch_normalize=1
activation=linear
[convolutional]
filters=144
size=5
groups=144
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=144
size=1
stride=1
pad=1
batch_normalize=1
activation=linear
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 0,1,2
anchors = 12, 18, 37, 49, 52,132, 115, 73, 119,199, 242,238
classes=80
num=6
jitter=.15
ignore_thresh = .5
truth_thresh = 1
random=0
scale_x_y = 1.00
iou_thresh=0.213
cls_normalizer=1.0
iou_normalizer=0.07
iou_loss=ciou
nms_kind=greedynms
beta_nms=0.6

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946
samples/sample_c++/module_sample/liveview/data/yolo-fastest-1.1_coco/yolo-fastest-1.1.cfg Normal file
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@ -0,0 +1,946 @@
[net]
batch=32
subdivisions=1
width=320
height=320
channels=3
momentum=0.949
decay=0.0005
angle=0
saturation=1.5
exposure=1.5
hue=.1
learning_rate=0.001
burn_in=4000
max_batches=500000
policy=steps
steps=400000,450000
scales=.1,.1
[convolutional]
filters=8
size=3
pad=1
stride=2
batch_normalize=1
activation=leaky
[convolutional]
filters=8
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=8
filters=8
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=4
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[convolutional]
filters=8
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=8
filters=8
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=4
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.15
[shortcut]
from=-5
activation=linear
[convolutional]
filters=24
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=24
filters=24
size=3
pad=1
stride=2
batch_normalize=1
activation=leaky
[convolutional]
filters=8
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[convolutional]
filters=32
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=32
filters=32
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=8
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.15
[shortcut]
from=-5
activation=linear
[convolutional]
filters=32
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=32
filters=32
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=8
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.15
[shortcut]
from=-5
activation=linear
[convolutional]
filters=32
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=32
filters=32
size=3
pad=1
stride=2
batch_normalize=1
activation=leaky
[convolutional]
filters=8
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[convolutional]
filters=48
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=48
filters=48
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=8
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.15
[shortcut]
from=-5
activation=linear
[convolutional]
filters=48
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=48
filters=48
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=8
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.15
[shortcut]
from=-5
activation=linear
[convolutional]
filters=48
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=48
filters=48
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=16
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[convolutional]
filters=96
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=96
filters=96
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=16
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.15
[shortcut]
from=-5
activation=linear
[convolutional]
filters=96
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=96
filters=96
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=16
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.15
[shortcut]
from=-5
activation=linear
[convolutional]
filters=96
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=96
filters=96
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=16
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.15
[shortcut]
from=-5
activation=linear
[convolutional]
filters=96
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=96
filters=96
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=16
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.15
[shortcut]
from=-5
activation=linear
[convolutional]
filters=96
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=96
filters=96
size=3
pad=1
stride=2
batch_normalize=1
activation=leaky
[convolutional]
filters=24
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[convolutional]
filters=136
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=136
filters=136
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=24
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.15
[shortcut]
from=-5
activation=linear
[convolutional]
filters=136
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=136
filters=136
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=24
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.15
[shortcut]
from=-5
activation=linear
[convolutional]
filters=136
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=136
filters=136
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=24
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.15
[shortcut]
from=-5
activation=linear
[convolutional]
filters=136
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=136
filters=136
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=24
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.15
[shortcut]
from=-5
activation=linear
[convolutional]
filters=136
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=136
filters=136
size=3
pad=1
stride=2
batch_normalize=1
activation=leaky
[convolutional]
filters=48
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[convolutional]
filters=224
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=224
filters=224
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=48
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.15
[shortcut]
from=-5
activation=linear
[convolutional]
filters=224
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=224
filters=224
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=48
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.15
[shortcut]
from=-5
activation=linear
[convolutional]
filters=224
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=224
filters=224
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=48
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.15
[shortcut]
from=-5
activation=linear
[convolutional]
filters=224
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=224
filters=224
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=48
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.15
[shortcut]
from=-5
activation=linear
[convolutional]
filters=224
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
groups=224
filters=224
size=3
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=48
size=1
stride=1
pad=0
batch_normalize=1
activation=linear
[dropout]
probability=.15
[shortcut]
from=-5
activation=linear
###############
[maxpool]
stride=1
size=3
[route]
layers=-2
[maxpool]
stride=1
size=5
[route]
layers=-4
[maxpool]
stride=1
size=9
[route]
layers=-1,-3,-5,-6
### End SPP ###
###############
[convolutional]
filters=96
size=1
stride=1
pad=0
batch_normalize=1
activation=leaky
[convolutional]
filters=96
size=5
groups=96
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=96
size=1
stride=1
pad=1
batch_normalize=1
activation=linear
[convolutional]
filters=96
size=5
groups=96
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=96
size=1
stride=1
pad=1
batch_normalize=1
activation=linear
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 3,4,5
anchors = 12, 18, 37, 49, 52,132, 115, 73, 119,199, 242,238
classes=80
num=6
jitter=.15
ignore_thresh = .5
truth_thresh = 1
random=0
scale_x_y = 1.0
iou_thresh=0.213
cls_normalizer=1.0
iou_normalizer=0.07
iou_loss=ciou
nms_kind=greedynms
beta_nms=0.6
[route]
layers = -7
[upsample]
stride = 2
[route]
layers=-1,80
[convolutional]
filters=120
size=5
groups=120
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=120
size=1
stride=1
pad=1
batch_normalize=1
activation=linear
[convolutional]
filters=120
size=5
groups=120
stride=1
pad=1
batch_normalize=1
activation=leaky
[convolutional]
filters=120
size=1
stride=1
pad=1
batch_normalize=1
activation=linear
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 0,1,2
anchors = 12, 18, 37, 49, 52,132, 115, 73, 119,199, 242,238
classes=80
num=6
jitter=.15
ignore_thresh = .5
truth_thresh = 1
random=0
scale_x_y = 1.00
iou_thresh=0.213
cls_normalizer=1.0
iou_normalizer=0.07
iou_loss=ciou
nms_kind=greedynms
beta_nms=0.6

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