tane@xavier:~/git/darknet_tk$ ./darknet export ../maui/yolov3-tiny-sml-rdm.cfg ../maui/yolov3-tiny-sml-rdm_best.weights layers GPU isn't used OpenCV version: 4.1.1 mini_batch = 1, batch = 1, time_steps = 1, train = 1 layer filters size/strd(dil) input output 0 conv 16 3 x 3/ 1 1920 x1088 x 3 -> 1920 x1088 x 16 1.805 BF 1 max 2x 2/ 2 1920 x1088 x 16 -> 960 x 544 x 16 0.033 BF 2 conv 32 3 x 3/ 1 960 x 544 x 16 -> 960 x 544 x 32 4.813 BF 3 max 2x 2/ 2 960 x 544 x 32 -> 480 x 272 x 32 0.017 BF 4 conv 64 3 x 3/ 1 480 x 272 x 32 -> 480 x 272 x 64 4.813 BF 5 max 2x 2/ 2 480 x 272 x 64 -> 240 x 136 x 64 0.008 BF 6 conv 128 3 x 3/ 1 240 x 136 x 64 -> 240 x 136 x 128 4.813 BF 7 max 2x 2/ 2 240 x 136 x 128 -> 120 x 68 x 128 0.004 BF 8 conv 256 3 x 3/ 1 120 x 68 x 128 -> 120 x 68 x 256 4.813 BF 9 max 2x 2/ 2 120 x 68 x 256 -> 60 x 34 x 256 0.002 BF 10 conv 512 3 x 3/ 1 60 x 34 x 256 -> 60 x 34 x 512 4.813 BF 11 max 2x 2/ 1 60 x 34 x 512 -> 60 x 34 x 512 0.004 BF 12 conv 1024 3 x 3/ 1 60 x 34 x 512 -> 60 x 34 x1024 19.252 BF 13 conv 256 1 x 1/ 1 60 x 34 x1024 -> 60 x 34 x 256 1.070 BF 14 conv 512 3 x 3/ 1 60 x 34 x 256 -> 60 x 34 x 512 4.813 BF 15 conv 18 1 x 1/ 1 60 x 34 x 512 -> 60 x 34 x 18 0.038 BF 16 yolo [yolo] params: iou loss: mse (2), iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00 17 route 13 -> 60 x 34 x 256 18 conv 128 1 x 1/ 1 60 x 34 x 256 -> 60 x 34 x 128 0.134 BF 19 upsample 2x 60 x 34 x 128 -> 120 x 68 x 128 20 route 19 8 -> 120 x 68 x 384 21 conv 256 3 x 3/ 1 120 x 68 x 384 -> 120 x 68 x 256 14.439 BF 22 conv 18 1 x 1/ 1 120 x 68 x 256 -> 120 x 68 x 18 0.075 BF 23 yolo [yolo] params: iou loss: mse (2), iou_norm: 0.75, cls_norm: 1.00, scale_x_y: 1.00 Total BFLOPS 65.758 avg_outputs = 3921220 Loading weights from ../maui/yolov3-tiny-sml-rdm_best.weights... seen 64, trained: 372 K-images (5 Kilo-batches_64) Done! Loaded 24 layers from weights-file n: 0, type 0 Convolutional weights: 432, biases: 16, batch_normalize: 1, groups: 1 write binary layers/c0.bin n: 1, type 3 export MAXPOOL n: 2, type 0 Convolutional weights: 4608, biases: 32, batch_normalize: 1, groups: 1 write binary layers/c2.bin n: 3, type 3 export MAXPOOL n: 4, type 0 Convolutional weights: 18432, biases: 64, batch_normalize: 1, groups: 1 write binary layers/c4.bin n: 5, type 3 export MAXPOOL n: 6, type 0 Convolutional weights: 73728, biases: 128, batch_normalize: 1, groups: 1 write binary layers/c6.bin n: 7, type 3 export MAXPOOL n: 8, type 0 Convolutional weights: 294912, biases: 256, batch_normalize: 1, groups: 1 write binary layers/c8.bin n: 9, type 3 export MAXPOOL n: 10, type 0 Convolutional weights: 1179648, biases: 512, batch_normalize: 1, groups: 1 write binary layers/c10.bin n: 11, type 3 export MAXPOOL n: 12, type 0 Convolutional weights: 4718592, biases: 1024, batch_normalize: 1, groups: 1 write binary layers/c12.bin n: 13, type 0 Convolutional weights: 262144, biases: 256, batch_normalize: 1, groups: 1 write binary layers/c13.bin n: 14, type 0 Convolutional weights: 1179648, biases: 512, batch_normalize: 1, groups: 1 write binary layers/c14.bin n: 15, type 0 Convolutional weights: 9216, biases: 18, batch_normalize: 0, groups: 1 write binary layers/c15.bin n: 16, type 27 export YOLO mask: 3 biases: 12 mask 3.000000 mask 4.000000 mask 5.000000 anchor 10.000000 anchor 14.000000 anchor 23.000000 anchor 27.000000 anchor 37.000000 anchor 58.000000 anchor 81.000000 anchor 82.000000 anchor 135.000000 anchor 169.000000 anchor 344.000000 anchor 319.000000 write binary layers/g16.bin n: 17, type 9 export ROUTE n: 18, type 0 Convolutional weights: 32768, biases: 128, batch_normalize: 1, groups: 1 write binary layers/c18.bin n: 19, type 32 export UPSAMPLE n: 20, type 9 export ROUTE n: 21, type 0 Convolutional weights: 884736, biases: 256, batch_normalize: 1, groups: 1 write binary layers/c21.bin n: 22, type 0 Convolutional weights: 4608, biases: 18, batch_normalize: 0, groups: 1 write binary layers/c22.bin n: 23, type 27 export YOLO mask: 3 biases: 12 mask 0.000000 mask 1.000000 mask 2.000000 anchor 10.000000 anchor 14.000000 anchor 23.000000 anchor 27.000000 anchor 37.000000 anchor 58.000000 anchor 81.000000 anchor 82.000000 anchor 135.000000 anchor 169.000000