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Darknet

Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.

This repository is borrowed heavily from https://github.com/pjreddie/darknet and https://github.com/AlexeyAB/darknet

new features

img1

img2

How to use

  • shufflenev2: for example basic unit
basicunit darknetcfg
  • yolov3 slimming

    Support: yolov3-tiny, yolov3, yolov3-spp

  ./darknet prune ./cfg/yolov3.cfg ./cfg/yolov3.weights -rate 0.3

the pruned cfg/weights are saved as ./cfg/yolov3_prune.cfg ./cfg/yolov3_prune.weights

Results

  • shufflenetv2

Top1: 0.52 Top5: 0.75

shuffle_imagenet.cfg : shuffle_imagenet.cfg.txt shuffle_imagenet.weights: google driver OR baidu pan (2eyp)

  • yolov3 slimming(coco)
yolov3 volume(MB) FLOPS Map(coco_val5k @0.5) finetuning iters parameters
before pruned 246 65 54.65 500k 1x
pruned @prune_rate=0.3 122 36.3 49.7 80k 0.5x
pruned @prune_rate=0.5 60.5 16 49.2 160k 0.25x
pruned @prune_rate=0.7 31 8.2 46 200k 0.125x
tiny-yolov3(official) 36 5.5 17.3
tiny-yolov3_3l 38 9.1 32
  • speed test(experiment on 1080Ti)
yolov3 volume(MB) FLOPS FPS(352x 288) FPS(960 x 540) FPS(1960 x 1080)
before pruned 246 65 60 57 53
pruned @prune_rate=0.3 122 36.3 82 78 76
pruned @prune_rate=0.5 60.5 16 107 105 97
pruned @prune_rate=0.7 31 8.2 130 123 120
  • speed test(experiment on XiaoMI laptop (Intel(R)Core(TM)i5-8250U CPU@1.6GHZ))
yolov3 volume(MB) FLOPS Inference Time
before pruned 246 65 436ms
pruned @prune_rate=0.3 122 36.3 230ms
pruned @prune_rate=0.5 60.5 16 125ms
pruned @prune_rate=0.7 31 8.2 70ms
  • download links to pruned cfgs/models

pruned @prune_rate=0.3: cfg(google driver),weight(google driver) OR cfg(baidupan)(s846),weight(baidupan)(eswd)

pruned @prune_rate=0.5: cfg(google driver),weight(google driver) OR cfg(baidupan)(y9gk), weight(baidupan)(5eqt)

pruned @prune_rate=0.7: cfg(google driver),weight(google driver) OR cfg(baidupan)(xh1d), weight(baidupan)(ump7)

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