pytorch-yolo3
Convert https://pjreddie.com/darknet/yolo/ into pytorch. Currently this repository works on python2 + pytorch 0.3.
Note: python3 is supported on python3 branch.
make detect.py works
wget https://pjreddie.com/media/files/yolov3.weights python detect.py cfg/yolov3.cfg yolov3.weights data/dog.jpg
You will see some output like this:
layer filters size input output 0 conv 32 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 32 1 conv 64 3 x 3 / 2 416 x 416 x 32 -> 208 x 208 x 64 2 conv 32 1 x 1 / 1 208 x 208 x 64 -> 208 x 208 x 32 3 conv 64 3 x 3 / 1 208 x 208 x 32 -> 208 x 208 x 64 4 shortcut 1 5 conv 128 3 x 3 / 2 208 x 208 x 64 -> 104 x 104 x 128 6 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64 7 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128 8 shortcut 5 9 conv 64 1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x 64 10 conv 128 3 x 3 / 1 104 x 104 x 64 -> 104 x 104 x 128 11 shortcut 8 12 conv 256 3 x 3 / 2 104 x 104 x 128 -> 52 x 52 x 256 13 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 14 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 15 shortcut 12 16 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 17 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 18 shortcut 15 19 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 20 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 21 shortcut 18 22 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 23 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 24 shortcut 21 25 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 26 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 27 shortcut 24 28 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 29 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 30 shortcut 27 31 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 32 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 33 shortcut 30 34 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 35 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 36 shortcut 33 37 conv 512 3 x 3 / 2 52 x 52 x 256 -> 26 x 26 x 512 38 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 39 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 40 shortcut 37 41 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 42 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 43 shortcut 40 44 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 45 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 46 shortcut 43 47 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 48 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 49 shortcut 46 50 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 51 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 52 shortcut 49 53 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 54 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 55 shortcut 52 56 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 57 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 58 shortcut 55 59 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 60 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 61 shortcut 58 62 conv 1024 3 x 3 / 2 26 x 26 x 512 -> 13 x 13 x1024 63 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 64 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 65 shortcut 62 66 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 67 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 68 shortcut 65 69 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 70 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 71 shortcut 68 72 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 73 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 74 shortcut 71 75 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 76 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 77 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 78 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 79 conv 512 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 512 80 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 81 conv 255 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 255 82 detection 83 route 79 84 conv 256 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 256 85 upsample * 2 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 88 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 89 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 90 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 91 conv 256 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 256 92 conv 512 3 x 3 / 1 26 x 26 x 256 -> 26 x 26 x 512 93 conv 255 1 x 1 / 1 26 x 26 x 512 -> 26 x 26 x 255 94 detection 95 route 91 96 conv 128 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 128 97 upsample * 2 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 128 100 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 101 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 102 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 103 conv 128 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 128 104 conv 256 3 x 3 / 1 52 x 52 x 128 -> 52 x 52 x 256 105 conv 255 1 x 1 / 1 52 x 52 x 256 -> 52 x 52 x 255 106 detection Loading weights from yolov3.weights... Done! data/dog.jpg: Predicted in 1.405360 seconds. dog: 0.999996 truck: 0.995232 bicycle: 0.999972 save plot results to predictions.jpg
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