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whynotw / YOLO_metric

Calculate mean Average Precision (mAP) and confusion matrix for object detection models. Bounding box information for groundtruth and prediction is YOLO training dataset format.
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I am getting this error when using on colab how can i fix it? #6

Open Tangkukuku opened 3 years ago

Tangkukuku commented 3 years ago

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

adamfahmi48 commented 2 years ago

i got the same error on google colab bang

``Try to load cfg: /content/gdrive/MyDrive/yolov3/yolov3_custom.cfg, weights: /content/gdrive/MyDrive/yolov3/backup/yolov3_custom_final_ori.weights, clear = 0 compute_capability = 370, cudnn_half = 0 layer filters size input output 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 32 1 x 1 / 1 208 x 208 x 64 -> 208 x 208 x 32 0.177 BF 3 conv 64 3 x 3 / 1 208 x 208 x 32 -> 208 x 208 x 64 1.595 BF 4 Shortcut Layer: 1 5 conv 128 3 x 3 / 2 208 x 208 x 64 -> 104 x 104 x 128 1.595 BF 6 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF 7 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128 1.595 BF 8 Shortcut Layer: 5 9 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF 10 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128 1.595 BF 11 Shortcut Layer: 8 12 conv 256 3 x 3 / 2 104 x 104 x 128 -> 52 x 52 x 256 1.595 BF 13 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 14 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 15 Shortcut Layer: 12 16 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 17 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 18 Shortcut Layer: 15 19 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 20 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 21 Shortcut Layer: 18 22 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 23 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 24 Shortcut Layer: 21 25 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 26 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 27 Shortcut Layer: 24 28 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 29 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 30 Shortcut Layer: 27 31 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 32 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 33 Shortcut Layer: 30 34 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 35 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 36 Shortcut Layer: 33 37 conv 512 3 x 3 / 2 52 x 52 x 256 -> 26 x 26 x 512 1.595 BF 38 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 39 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 40 Shortcut Layer: 37 41 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 42 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 43 Shortcut Layer: 40 44 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 45 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 46 Shortcut Layer: 43 47 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 48 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 49 Shortcut Layer: 46 50 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 51 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 52 Shortcut Layer: 49 53 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 54 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 55 Shortcut Layer: 52 56 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 57 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 58 Shortcut Layer: 55 59 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 60 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 61 Shortcut Layer: 58 62 conv 1024 3 x 3 / 2 26 x 26 x 512 -> 13 x 13 x1024 1.595 BF 63 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 64 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 65 Shortcut Layer: 62 66 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 67 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 68 Shortcut Layer: 65 69 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 70 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 71 Shortcut Layer: 68 72 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 73 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 74 Shortcut Layer: 71 75 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 76 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 77 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 78 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 79 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 80 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 81 conv 33 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 33 0.011 BF 82 yolo 83 route 79 84 conv 256 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 256 0.044 BF 85 upsample 2x 13 x 13 x 256 -> 26 x 26 x 256 86 route 85 61 87 conv 256 1 x 1 / 1 26 x 26 x 768 -> 26 x 26 x 256 0.266 BF 88 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 89 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 90 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 91 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 92 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 93 conv 33 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 33 0.023 BF 94 yolo 95 route 91 96 conv 128 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 128 0.044 BF 97 upsample 2x 26 x 26 x 128 -> 52 x 52 x 128 98 route 97 36 99 conv 128 1 x 1 / 1 52 x 52 x 384 -> 52 x 52 x 128 0.266 BF 100 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 101 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 102 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 103 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 104 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 105 conv 33 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 33 0.046 BF 106 yolo Total BFLOPS 65.326 Allocate additional workspace_size = 18.91 MB Loading weights from /content/gdrive/MyDrive/yolov3/backup/yolov3_custom_final_ori.weights... seen 64 CUDA status Error: file: ./src/convolutional_kernels.cu : () : line: 143 : build time: Oct 27 2021 - 22:54:58 CUDA Error: no kernel image is available for execution on the device python2: : Unknown error 1430997643

adamfahmi48 commented 2 years ago

i got the same error on google colab bang

``Try to load cfg: /content/gdrive/MyDrive/yolov3/yolov3_custom.cfg, weights: /content/gdrive/MyDrive/yolov3/backup/yolov3_custom_final_ori.weights, clear = 0 compute_capability = 370, cudnn_half = 0 layer filters size input output 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 32 1 x 1 / 1 208 x 208 x 64 -> 208 x 208 x 32 0.177 BF 3 conv 64 3 x 3 / 1 208 x 208 x 32 -> 208 x 208 x 64 1.595 BF 4 Shortcut Layer: 1 5 conv 128 3 x 3 / 2 208 x 208 x 64 -> 104 x 104 x 128 1.595 BF 6 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF 7 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128 1.595 BF 8 Shortcut Layer: 5 9 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF 10 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128 1.595 BF 11 Shortcut Layer: 8 12 conv 256 3 x 3 / 2 104 x 104 x 128 -> 52 x 52 x 256 1.595 BF 13 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 14 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 15 Shortcut Layer: 12 16 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 17 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 18 Shortcut Layer: 15 19 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 20 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 21 Shortcut Layer: 18 22 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 23 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 24 Shortcut Layer: 21 25 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 26 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 27 Shortcut Layer: 24 28 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 29 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 30 Shortcut Layer: 27 31 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 32 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 33 Shortcut Layer: 30 34 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 35 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 36 Shortcut Layer: 33 37 conv 512 3 x 3 / 2 52 x 52 x 256 -> 26 x 26 x 512 1.595 BF 38 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 39 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 40 Shortcut Layer: 37 41 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 42 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 43 Shortcut Layer: 40 44 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 45 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 46 Shortcut Layer: 43 47 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 48 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 49 Shortcut Layer: 46 50 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 51 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 52 Shortcut Layer: 49 53 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 54 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 55 Shortcut Layer: 52 56 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 57 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 58 Shortcut Layer: 55 59 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 60 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 61 Shortcut Layer: 58 62 conv 1024 3 x 3 / 2 26 x 26 x 512 -> 13 x 13 x1024 1.595 BF 63 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 64 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 65 Shortcut Layer: 62 66 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 67 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 68 Shortcut Layer: 65 69 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 70 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 71 Shortcut Layer: 68 72 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 73 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 74 Shortcut Layer: 71 75 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 76 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 77 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 78 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 79 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 80 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 81 conv 33 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 33 0.011 BF 82 yolo 83 route 79 84 conv 256 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 256 0.044 BF 85 upsample 2x 13 x 13 x 256 -> 26 x 26 x 256 86 route 85 61 87 conv 256 1 x 1 / 1 26 x 26 x 768 -> 26 x 26 x 256 0.266 BF 88 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 89 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 90 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 91 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 92 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 93 conv 33 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 33 0.023 BF 94 yolo 95 route 91 96 conv 128 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 128 0.044 BF 97 upsample 2x 26 x 26 x 128 -> 52 x 52 x 128 98 route 97 36 99 conv 128 1 x 1 / 1 52 x 52 x 384 -> 52 x 52 x 128 0.266 BF 100 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 101 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 102 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 103 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 104 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 105 conv 33 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 33 0.046 BF 106 yolo Total BFLOPS 65.326 Allocate additional workspace_size = 18.91 MB Loading weights from /content/gdrive/MyDrive/yolov3/backup/yolov3_custom_final_ori.weights... seen 64 CUDA status Error: file: ./src/convolutional_kernels.cu : () : line: 143 : build time: Oct 27 2021 - 22:54:58 CUDA Error: no kernel image is available for execution on the device python2: : Unknown error 1430997643

even though I've success to run this repository before, but now i get this error too

bivacandra commented 2 years ago

i got the same error on google colab bang ``Try to load cfg: /content/gdrive/MyDrive/yolov3/yolov3_custom.cfg, weights: /content/gdrive/MyDrive/yolov3/backup/yolov3_custom_final_ori.weights, clear = 0 compute_capability = 370, cudnn_half = 0 layer filters size input output 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 32 1 x 1 / 1 208 x 208 x 64 -> 208 x 208 x 32 0.177 BF 3 conv 64 3 x 3 / 1 208 x 208 x 32 -> 208 x 208 x 64 1.595 BF 4 Shortcut Layer: 1 5 conv 128 3 x 3 / 2 208 x 208 x 64 -> 104 x 104 x 128 1.595 BF 6 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF 7 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128 1.595 BF 8 Shortcut Layer: 5 9 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF 10 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128 1.595 BF 11 Shortcut Layer: 8 12 conv 256 3 x 3 / 2 104 x 104 x 128 -> 52 x 52 x 256 1.595 BF 13 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 14 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 15 Shortcut Layer: 12 16 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 17 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 18 Shortcut Layer: 15 19 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 20 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 21 Shortcut Layer: 18 22 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 23 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 24 Shortcut Layer: 21 25 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 26 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 27 Shortcut Layer: 24 28 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 29 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 30 Shortcut Layer: 27 31 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 32 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 33 Shortcut Layer: 30 34 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 35 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 36 Shortcut Layer: 33 37 conv 512 3 x 3 / 2 52 x 52 x 256 -> 26 x 26 x 512 1.595 BF 38 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 39 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 40 Shortcut Layer: 37 41 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 42 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 43 Shortcut Layer: 40 44 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 45 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 46 Shortcut Layer: 43 47 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 48 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 49 Shortcut Layer: 46 50 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 51 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 52 Shortcut Layer: 49 53 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 54 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 55 Shortcut Layer: 52 56 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 57 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 58 Shortcut Layer: 55 59 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 60 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 61 Shortcut Layer: 58 62 conv 1024 3 x 3 / 2 26 x 26 x 512 -> 13 x 13 x1024 1.595 BF 63 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 64 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 65 Shortcut Layer: 62 66 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 67 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 68 Shortcut Layer: 65 69 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 70 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 71 Shortcut Layer: 68 72 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 73 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 74 Shortcut Layer: 71 75 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 76 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 77 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 78 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 79 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 0.177 BF 80 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF 81 conv 33 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 33 0.011 BF 82 yolo 83 route 79 84 conv 256 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 256 0.044 BF 85 upsample 2x 13 x 13 x 256 -> 26 x 26 x 256 86 route 85 61 87 conv 256 1 x 1 / 1 26 x 26 x 768 -> 26 x 26 x 256 0.266 BF 88 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 89 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 90 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 91 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF 92 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF 93 conv 33 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 33 0.023 BF 94 yolo 95 route 91 96 conv 128 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 128 0.044 BF 97 upsample 2x 26 x 26 x 128 -> 52 x 52 x 128 98 route 97 36 99 conv 128 1 x 1 / 1 52 x 52 x 384 -> 52 x 52 x 128 0.266 BF 100 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 101 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 102 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 103 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF 104 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF 105 conv 33 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 33 0.046 BF 106 yolo Total BFLOPS 65.326 Allocate additional workspace_size = 18.91 MB Loading weights from /content/gdrive/MyDrive/yolov3/backup/yolov3_custom_final_ori.weights... seen 64 CUDA status Error: file: ./src/convolutional_kernels.cu : () : line: 143 : build time: Oct 27 2021 - 22:54:58 CUDA Error: no kernel image is available for execution on the device python2: : Unknown error 1430997643

even though I've success to run this repository before, but now i get this error too

Try not to use the CUDA service on Google Colab by doing this:

Edit the Makefile file, change GPU=0 and CUDNN=0

Try make clean and re-make and try running detection again