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Olá boa tarde. Então tentei diminuir o threshold e mesmo assim não apareceu o bounding box no prediction.img. Onde consigo visualizar as iterações que foram realizadas no treinamento ? Abaixo está o log quando rodo o detector do treinamento.
Obrigado pela ajuda mestre!
Abraços
CUDA-version: 11010 (11020), cuDNN: 7.6.5, CUDNN_HALF=1, GPU count: 1 CUDNN_HALF=1 OpenCV version: 3.2.0 0 : compute_capability = 600, cudnn_half = 0, GPU: Tesla P100-PCIE-16GB 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 = 52.43 MB Loading weights from ./backup/yolov4-custom_best.weights... seen 64, trained: 294 K-images (4 Kilo-batches_64) Done! Loaded 162 layers from weights-file Detection layer: 139 - type = 28 Detection layer: 150 - type = 28 Detection layer: 161 - type = 28 ./training/IMG7.JPG: Predicted in 20.193000 milli-seconds.
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