yolov3-spp3 layer filters size input output 0 conv 32 3 x 3 / 1 608 x 608 x 3 -> 608 x 608 x 32 1 conv 64 3 x 3 / 2 608 x 608 x 32 -> 304 x 304 x 64 2 conv 32 1 x 1 / 1 304 x 304 x 64 -> 304 x 304 x 32 3 conv 64 3 x 3 / 1 304 x 304 x 32 -> 304 x 304 x 64 4 Shortcut Layer: 1 5 conv 128 3 x 3 / 2 304 x 304 x 64 -> 152 x 152 x 128 6 conv 64 1 x 1 / 1 152 x 152 x 128 -> 152 x 152 x 64 7 conv 128 3 x 3 / 1 152 x 152 x 64 -> 152 x 152 x 128 8 Shortcut Layer: 5 9 conv 64 1 x 1 / 1 152 x 152 x 128 -> 152 x 152 x 64 10 conv 128 3 x 3 / 1 152 x 152 x 64 -> 152 x 152 x 128 11 Shortcut Layer: 8 12 conv 256 3 x 3 / 2 152 x 152 x 128 -> 76 x 76 x 256 13 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 14 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 15 Shortcut Layer: 12 16 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 17 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 18 Shortcut Layer: 15 19 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 20 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 21 Shortcut Layer: 18 22 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 23 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 24 Shortcut Layer: 21 25 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 26 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 27 Shortcut Layer: 24 28 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 29 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 30 Shortcut Layer: 27 31 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 32 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 33 Shortcut Layer: 30 34 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 35 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 36 Shortcut Layer: 33 37 conv 512 3 x 3 / 2 76 x 76 x 256 -> 38 x 38 x 512 38 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 39 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 40 Shortcut Layer: 37 41 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 42 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 43 Shortcut Layer: 40 44 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 45 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 46 Shortcut Layer: 43 47 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 48 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 49 Shortcut Layer: 46 50 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 51 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 52 Shortcut Layer: 49 53 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 54 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 55 Shortcut Layer: 52 56 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 57 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 58 Shortcut Layer: 55 59 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 60 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 61 Shortcut Layer: 58 62 conv 1024 3 x 3 / 2 38 x 38 x 512 -> 19 x 19 x1024 63 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 64 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 65 Shortcut Layer: 62 66 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 67 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 68 Shortcut Layer: 65 69 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 70 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 71 Shortcut Layer: 68 72 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 73 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 74 Shortcut Layer: 71 75 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 76 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 77 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 78 max 5 x 5 / 1 19 x 19 x 512 -> 23 x 23 x 512 79 route 77 80 max 9 x 9 / 1 19 x 19 x 512 -> 27 x 27 x 512 81 route 77 82 max 13 x 13 / 1 19 x 19 x 512 -> 31 x 31 x 512 83 route 82 80 78 77 84 Layer before convolutional layer must output image.: Success darknet: ./src/utils.c:237: error: Assertion0' failed.
Aborted (core dumped)
`
Hi,
when I run this code:
darknet detector train VisDrone2019/drone.data cfg/yolov3-spp3.cfg darknet53.conv.74.weightsI get this error
yolov3-spp3 layer filters size input output 0 conv 32 3 x 3 / 1 608 x 608 x 3 -> 608 x 608 x 32 1 conv 64 3 x 3 / 2 608 x 608 x 32 -> 304 x 304 x 64 2 conv 32 1 x 1 / 1 304 x 304 x 64 -> 304 x 304 x 32 3 conv 64 3 x 3 / 1 304 x 304 x 32 -> 304 x 304 x 64 4 Shortcut Layer: 1 5 conv 128 3 x 3 / 2 304 x 304 x 64 -> 152 x 152 x 128 6 conv 64 1 x 1 / 1 152 x 152 x 128 -> 152 x 152 x 64 7 conv 128 3 x 3 / 1 152 x 152 x 64 -> 152 x 152 x 128 8 Shortcut Layer: 5 9 conv 64 1 x 1 / 1 152 x 152 x 128 -> 152 x 152 x 64 10 conv 128 3 x 3 / 1 152 x 152 x 64 -> 152 x 152 x 128 11 Shortcut Layer: 8 12 conv 256 3 x 3 / 2 152 x 152 x 128 -> 76 x 76 x 256 13 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 14 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 15 Shortcut Layer: 12 16 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 17 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 18 Shortcut Layer: 15 19 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 20 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 21 Shortcut Layer: 18 22 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 23 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 24 Shortcut Layer: 21 25 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 26 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 27 Shortcut Layer: 24 28 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 29 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 30 Shortcut Layer: 27 31 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 32 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 33 Shortcut Layer: 30 34 conv 128 1 x 1 / 1 76 x 76 x 256 -> 76 x 76 x 128 35 conv 256 3 x 3 / 1 76 x 76 x 128 -> 76 x 76 x 256 36 Shortcut Layer: 33 37 conv 512 3 x 3 / 2 76 x 76 x 256 -> 38 x 38 x 512 38 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 39 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 40 Shortcut Layer: 37 41 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 42 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 43 Shortcut Layer: 40 44 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 45 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 46 Shortcut Layer: 43 47 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 48 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 49 Shortcut Layer: 46 50 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 51 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 52 Shortcut Layer: 49 53 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 54 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 55 Shortcut Layer: 52 56 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 57 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 58 Shortcut Layer: 55 59 conv 256 1 x 1 / 1 38 x 38 x 512 -> 38 x 38 x 256 60 conv 512 3 x 3 / 1 38 x 38 x 256 -> 38 x 38 x 512 61 Shortcut Layer: 58 62 conv 1024 3 x 3 / 2 38 x 38 x 512 -> 19 x 19 x1024 63 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 64 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 65 Shortcut Layer: 62 66 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 67 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 68 Shortcut Layer: 65 69 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 70 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 71 Shortcut Layer: 68 72 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 73 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 74 Shortcut Layer: 71 75 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 76 conv 1024 3 x 3 / 1 19 x 19 x 512 -> 19 x 19 x1024 77 conv 512 1 x 1 / 1 19 x 19 x1024 -> 19 x 19 x 512 78 max 5 x 5 / 1 19 x 19 x 512 -> 23 x 23 x 512 79 route 77 80 max 9 x 9 / 1 19 x 19 x 512 -> 27 x 27 x 512 81 route 77 82 max 13 x 13 / 1 19 x 19 x 512 -> 31 x 31 x 512 83 route 82 80 78 77 84 Layer before convolutional layer must output image.: Success darknet: ./src/utils.c:237: error: Assertion0' failed. Aborted (core dumped) `