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All Projects → marvis → Pytorch Caffe Darknet Convert

marvis / Pytorch Caffe Darknet Convert

Licence: mit
convert between pytorch, caffe prototxt/weights and darknet cfg/weights

Programming Languages

python
139335 projects - #7 most used programming language

Labels

pytorchcaffeyolodarknetconvertyolo2

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pytorch-caffe-darknet-convert

This repository is specially designed for pytorch-yolo2 to convert pytorch trained model to any platform. It can also be used as a common model converter between pytorch, caffe and darknet.

  • [x] darknet2pytorch : use darknet.py to load darknet model directly
  • [x] caffe2pytorch : use caffenet.py to load caffe model directly, furthur supports moved to caffe2pytorch
  • [x] darknet2caffe
  • [x] caffe2darknet
  • [x] pytorch2caffe
  • [x] pytorch2darknet : pytorch2caffe then caffe2darknet
  • [x] shrink_bn_caffe : shrink batchnorm and scale layer in caffe model automatically

Convert pytorch -> caffe -> darknet

1. python main.py -a resnet50-pytorch --pretrained -e /home/xiaohang/ImageNet/
=> using pre-trained model 'resnet50-pytorch'
Test: [0/196]   Time 14.016 (14.016)    Loss 0.4863 (0.4863)    [email protected] 85.938 (85.938)  [email protected] 97.656 (97.656)
Test: [10/196]  Time 0.179 (1.616)      Loss 0.9623 (0.6718)    [email protected] 76.562 (82.919)  [email protected] 93.359 (95.561)
Test: [20/196]  Time 0.165 (1.152)      Loss 0.7586 (0.6859)    [email protected] 86.328 (82.738)  [email protected] 92.578 (95.424)
Test: [30/196]  Time 0.253 (1.061)      Loss 0.7881 (0.6409)    [email protected] 80.469 (84.073)  [email protected] 95.312 (95.804)
Test: [40/196]  Time 0.648 (0.973)      Loss 0.6530 (0.6863)    [email protected] 82.812 (82.336)  [email protected] 96.484 (95.798)
Test: [50/196]  Time 0.153 (0.938)      Loss 0.4764 (0.6844)    [email protected] 89.062 (82.207)  [email protected] 97.266 (95.910)
Test: [60/196]  Time 0.149 (0.908)      Loss 0.9198 (0.6984)    [email protected] 76.172 (81.807)  [email protected] 95.312 (95.959)
Test: [70/196]  Time 0.645 (0.903)      Loss 0.7103 (0.6851)    [email protected] 78.516 (82.042)  [email protected] 96.094 (96.072)
Test: [80/196]  Time 0.663 (0.884)      Loss 1.4683 (0.7112)    [email protected] 62.109 (81.520)  [email protected] 88.672 (95.737)
Test: [90/196]  Time 1.429 (0.881)      Loss 1.8474 (0.7593)    [email protected] 57.031 (80.460)  [email protected] 86.719 (95.261)
Test: [100/196] Time 0.195 (0.859)      Loss 1.1329 (0.8115)    [email protected] 68.359 (79.297)  [email protected] 91.797 (94.694)
Test: [110/196] Time 1.109 (0.859)      Loss 0.8606 (0.8358)    [email protected] 77.734 (78.790)  [email protected] 93.750 (94.457)
Test: [120/196] Time 0.153 (0.851)      Loss 1.2403 (0.8538)    [email protected] 69.922 (78.483)  [email protected] 87.500 (94.150)
Test: [130/196] Time 2.340 (0.851)      Loss 0.7038 (0.8877)    [email protected] 80.469 (77.612)  [email protected] 96.484 (93.831)
Test: [140/196] Time 0.139 (0.839)      Loss 1.0392 (0.9057)    [email protected] 74.609 (77.263)  [email protected] 91.797 (93.628)
Test: [150/196] Time 2.273 (0.839)      Loss 1.0445 (0.9234)    [email protected] 75.781 (76.930)  [email protected] 90.234 (93.385)
Test: [160/196] Time 0.153 (0.830)      Loss 0.6993 (0.9374)    [email protected] 86.328 (76.672)  [email protected] 94.141 (93.180)
Test: [170/196] Time 2.016 (0.831)      Loss 0.6132 (0.9542)    [email protected] 82.422 (76.263)  [email protected] 97.656 (93.012)
Test: [180/196] Time 0.926 (0.823)      Loss 1.2884 (0.9700)    [email protected] 69.531 (75.930)  [email protected] 92.969 (92.872)
Test: [190/196] Time 1.609 (0.821)      Loss 1.1864 (0.9686)    [email protected] 67.188 (75.920)  [email protected] 94.922 (92.899)
 * [email protected] 76.022 [email protected] 92.934
2. python pytorch2caffe.py 
3. python main.py -a resnet50-pytorch2caffe --pretrained -e /home/xiaohang/ImageNet/
=> using pre-trained model 'resnet50-pytorch2caffe'
load weights resnet50-pytorch2caffe.caffemodel
Loading caffemodel:  resnet50-pytorch2caffe.caffemodel
Test: [0/196]   Time 14.528 (14.528)    Loss 0.4863 (0.4863)    [email protected] 85.938 (85.938)  [email protected] 97.656 (97.656)
Test: [10/196]  Time 0.356 (1.678)      Loss 0.9623 (0.6718)    [email protected] 76.562 (82.919)  [email protected] 93.359 (95.561)
Test: [20/196]  Time 0.183 (1.206)      Loss 0.7586 (0.6859)    [email protected] 86.328 (82.738)  [email protected] 92.578 (95.424)
Test: [30/196]  Time 0.428 (1.112)      Loss 0.7881 (0.6409)    [email protected] 80.469 (84.073)  [email protected] 95.312 (95.804)
Test: [40/196]  Time 0.820 (1.022)      Loss 0.6530 (0.6863)    [email protected] 82.812 (82.336)  [email protected] 96.484 (95.798)
Test: [50/196]  Time 0.290 (0.978)      Loss 0.4764 (0.6844)    [email protected] 89.062 (82.207)  [email protected] 97.266 (95.910)
Test: [60/196]  Time 0.477 (0.941)      Loss 0.9198 (0.6984)    [email protected] 76.172 (81.807)  [email protected] 95.312 (95.959)
Test: [70/196]  Time 0.246 (0.927)      Loss 0.7103 (0.6851)    [email protected] 78.516 (82.042)  [email protected] 96.094 (96.072)
Test: [80/196]  Time 0.877 (0.910)      Loss 1.4683 (0.7112)    [email protected] 62.109 (81.520)  [email protected] 88.672 (95.737)
Test: [90/196]  Time 0.752 (0.906)      Loss 1.8474 (0.7593)    [email protected] 57.031 (80.460)  [email protected] 86.719 (95.261)
Test: [100/196] Time 0.156 (0.883)      Loss 1.1329 (0.8115)    [email protected] 68.359 (79.297)  [email protected] 91.797 (94.694)
Test: [110/196] Time 0.324 (0.882)      Loss 0.8606 (0.8358)    [email protected] 77.734 (78.790)  [email protected] 93.750 (94.457)
Test: [120/196] Time 0.486 (0.878)      Loss 1.2403 (0.8538)    [email protected] 69.922 (78.483)  [email protected] 87.500 (94.150)
Test: [130/196] Time 1.067 (0.871)      Loss 0.7038 (0.8877)    [email protected] 80.469 (77.612)  [email protected] 96.484 (93.831)
Test: [140/196] Time 0.261 (0.863)      Loss 1.0392 (0.9057)    [email protected] 74.609 (77.263)  [email protected] 91.797 (93.628)
Test: [150/196] Time 0.354 (0.852)      Loss 1.0445 (0.9234)    [email protected] 75.781 (76.930)  [email protected] 90.234 (93.385)
Test: [160/196] Time 0.152 (0.851)      Loss 0.6993 (0.9374)    [email protected] 86.328 (76.672)  [email protected] 94.141 (93.180)
Test: [170/196] Time 0.688 (0.842)      Loss 0.6132 (0.9542)    [email protected] 82.422 (76.263)  [email protected] 97.656 (93.012)
Test: [180/196] Time 0.244 (0.839)      Loss 1.2884 (0.9700)    [email protected] 69.531 (75.930)  [email protected] 92.969 (92.872)
Test: [190/196] Time 0.383 (0.834)      Loss 1.1864 (0.9686)    [email protected] 67.188 (75.920)  [email protected] 94.922 (92.899)
 * [email protected] 76.022 [email protected] 92.934
4. python caffe2darknet.py resnet50-pytorch2caffe.prototxt resnet50-pytorch2caffe.caffemodel resnet50-caffe2darknet.cfg resnet50-caffe2darknet.weights
5. python main.py -a resnet50-caffe2darknet --pretrained -e /home/xiaohang/ImageNet/        
=> using pre-trained model 'resnet50-caffe2darknet'
load weights from resnet50-caffe2darknet.weights
Test: [0/196]   Time 15.418 (15.418)    Loss 0.4863 (0.4863)    [email protected] 85.938 (85.938)  [email protected] 97.656 (97.656)
Test: [10/196]  Time 0.393 (1.760)      Loss 0.9623 (0.6718)    [email protected] 76.562 (82.919)  [email protected] 93.359 (95.561)
Test: [20/196]  Time 0.264 (1.241)      Loss 0.7586 (0.6859)    [email protected] 86.328 (82.738)  [email protected] 92.578 (95.424)
Test: [30/196]  Time 0.160 (1.123)      Loss 0.7881 (0.6409)    [email protected] 80.469 (84.073)  [email protected] 95.312 (95.804)
Test: [40/196]  Time 0.789 (1.020)      Loss 0.6530 (0.6863)    [email protected] 82.812 (82.336)  [email protected] 96.484 (95.798)
Test: [50/196]  Time 0.354 (0.983)      Loss 0.4764 (0.6844)    [email protected] 89.062 (82.207)  [email protected] 97.266 (95.910)
Test: [60/196]  Time 0.458 (0.946)      Loss 0.9198 (0.6984)    [email protected] 76.172 (81.807)  [email protected] 95.312 (95.959)
Test: [70/196]  Time 0.848 (0.936)      Loss 0.7103 (0.6851)    [email protected] 78.516 (82.042)  [email protected] 96.094 (96.072)
Test: [80/196]  Time 0.993 (0.918)      Loss 1.4683 (0.7112)    [email protected] 62.109 (81.520)  [email protected] 88.672 (95.737)
Test: [90/196]  Time 1.750 (0.911)      Loss 1.8474 (0.7593)    [email protected] 57.031 (80.460)  [email protected] 86.719 (95.261)
Test: [100/196] Time 0.160 (0.889)      Loss 1.1329 (0.8115)    [email protected] 68.359 (79.297)  [email protected] 91.797 (94.694)
Test: [110/196] Time 1.261 (0.883)      Loss 0.8606 (0.8358)    [email protected] 77.734 (78.790)  [email protected] 93.750 (94.457)
Test: [120/196] Time 0.667 (0.874)      Loss 1.2403 (0.8538)    [email protected] 69.922 (78.483)  [email protected] 87.500 (94.150)
Test: [130/196] Time 1.216 (0.867)      Loss 0.7038 (0.8877)    [email protected] 80.469 (77.612)  [email protected] 96.484 (93.831)
Test: [140/196] Time 0.166 (0.857)      Loss 1.0392 (0.9057)    [email protected] 74.609 (77.263)  [email protected] 91.797 (93.628)
Test: [150/196] Time 1.123 (0.850)      Loss 1.0445 (0.9234)    [email protected] 75.781 (76.930)  [email protected] 90.234 (93.385)
Test: [160/196] Time 0.161 (0.845)      Loss 0.6993 (0.9374)    [email protected] 86.328 (76.672)  [email protected] 94.141 (93.180)
Test: [170/196] Time 0.345 (0.837)      Loss 0.6132 (0.9542)    [email protected] 82.422 (76.263)  [email protected] 97.656 (93.012)
Test: [180/196] Time 1.152 (0.839)      Loss 1.2884 (0.9700)    [email protected] 69.531 (75.930)  [email protected] 92.969 (92.872)
Test: [190/196] Time 0.165 (0.829)      Loss 1.1864 (0.9686)    [email protected] 67.188 (75.920)  [email protected] 94.922 (92.899)
 * [email protected] 76.022 [email protected] 92.934
6. python shrink_bn_caffe.py resnet50-pytorch2caffe.prototxt resnet50-pytorch2caffe.caffemodel resnet50-pytorch2caffe.nobn.prototxt resnet50-pytorch2caffe.nobn.caffemodel
7. python main.py -a resnet50-pytorch2caffe.nobn --pretrained -e /home/xiaohang/ImageNet/
Test: [0/196]   Time 29.615 (29.615)    Loss 0.4863 (0.4863)    [email protected] 85.938 (85.938)  [email protected] 97.656 (97.656)
Test: [10/196]  Time 0.470 (3.075)      Loss 0.9623 (0.6718)    [email protected] 76.562 (82.919)  [email protected] 93.359 (95.561)
Test: [20/196]  Time 0.221 (1.940)      Loss 0.7586 (0.6859)    [email protected] 86.328 (82.738)  [email protected] 92.578 (95.424)
Test: [30/196]  Time 0.890 (1.617)      Loss 0.7881 (0.6409)    [email protected] 80.469 (84.073)  [email protected] 95.312 (95.804)
Test: [40/196]  Time 1.176 (1.426)      Loss 0.6530 (0.6863)    [email protected] 82.812 (82.336)  [email protected] 96.484 (95.798)
Test: [50/196]  Time 1.331 (1.304)      Loss 0.4764 (0.6844)    [email protected] 89.062 (82.207)  [email protected] 97.266 (95.910)
Test: [60/196]  Time 0.520 (1.223)      Loss 0.9198 (0.6984)    [email protected] 76.172 (81.807)  [email protected] 95.312 (95.959)
Test: [70/196]  Time 0.397 (1.184)      Loss 0.7103 (0.6851)    [email protected] 78.516 (82.042)  [email protected] 96.094 (96.072)
Test: [80/196]  Time 0.666 (1.141)      Loss 1.4683 (0.7112)    [email protected] 62.109 (81.520)  [email protected] 88.672 (95.737)
Test: [90/196]  Time 0.759 (1.121)      Loss 1.8474 (0.7593)    [email protected] 57.031 (80.460)  [email protected] 86.719 (95.261)
Test: [100/196] Time 0.153 (1.082)      Loss 1.1329 (0.8115)    [email protected] 68.359 (79.297)  [email protected] 91.797 (94.694)
Test: [110/196] Time 0.511 (1.068)      Loss 0.8606 (0.8358)    [email protected] 77.734 (78.790)  [email protected] 93.750 (94.457)
Test: [120/196] Time 0.643 (1.057)      Loss 1.2403 (0.8538)    [email protected] 69.922 (78.483)  [email protected] 87.500 (94.150)
Test: [130/196] Time 1.309 (1.040)      Loss 0.7038 (0.8877)    [email protected] 80.469 (77.612)  [email protected] 96.484 (93.831)
Test: [140/196] Time 0.261 (1.021)      Loss 1.0392 (0.9057)    [email protected] 74.609 (77.263)  [email protected] 91.797 (93.628)
Test: [150/196] Time 1.744 (1.013)      Loss 1.0445 (0.9234)    [email protected] 75.781 (76.930)  [email protected] 90.234 (93.385)
Test: [160/196] Time 0.222 (0.997)      Loss 0.6993 (0.9374)    [email protected] 86.328 (76.672)  [email protected] 94.141 (93.180)
Test: [170/196] Time 1.306 (0.994)      Loss 0.6132 (0.9542)    [email protected] 82.422 (76.263)  [email protected] 97.656 (93.012)
Test: [180/196] Time 0.609 (0.978)      Loss 1.2884 (0.9700)    [email protected] 69.531 (75.930)  [email protected] 92.969 (92.872)
Test: [190/196] Time 0.505 (0.972)      Loss 1.1864 (0.9686)    [email protected] 67.188 (75.920)  [email protected] 94.922 (92.899)
 * [email protected] 76.022 [email protected] 92.934

Note:

  1. imagenet data is processed as described here
  2. to make pytorch2caffe.py work, you need to change the ceil function in caffe's pooling layer to floor

Convert pytorch -> darknet -> caffe

convert resnet50 from pytorch to darknet and then to caffe

1. python pytorch2darknet.py 
2. python main.py -a resnet50-darknet --pretrained -e /home/xiaohang/ImageNet/
=> using pre-trained model 'resnet50-darknet'
load weights from resnet50.weights
Test: [0/196]   Time 15.029 (15.029)    Loss 6.0965 (6.0965)    [email protected] 85.938 (85.938)  [email protected] 97.656 (97.656)
Test: [10/196]  Time 0.380 (1.716)      Loss 6.2165 (6.1346)    [email protected] 76.562 (82.919)  [email protected] 93.359 (95.561)
Test: [20/196]  Time 0.167 (1.205)      Loss 6.0981 (6.1388)    [email protected] 86.328 (82.738)  [email protected] 92.578 (95.424)
Test: [30/196]  Time 0.163 (1.100)      Loss 6.1633 (6.1244)    [email protected] 80.469 (84.073)  [email protected] 95.312 (95.804)
Test: [40/196]  Time 0.862 (1.009)      Loss 6.1777 (6.1473)    [email protected] 82.812 (82.336)  [email protected] 96.484 (95.798)
Test: [50/196]  Time 0.713 (0.965)      Loss 6.0856 (6.1510)    [email protected] 89.062 (82.207)  [email protected] 97.266 (95.910)
Test: [60/196]  Time 0.867 (0.936)      Loss 6.1982 (6.1557)    [email protected] 76.172 (81.807)  [email protected] 95.312 (95.959)
Test: [70/196]  Time 0.451 (0.917)      Loss 6.1979 (6.1513)    [email protected] 78.516 (82.042)  [email protected] 96.094 (96.072)
Test: [80/196]  Time 1.749 (0.909)      Loss 6.3671 (6.1568)    [email protected] 62.109 (81.520)  [email protected] 88.672 (95.737)
Test: [90/196]  Time 0.904 (0.892)      Loss 6.4027 (6.1684)    [email protected] 57.031 (80.460)  [email protected] 86.719 (95.261)
Test: [100/196] Time 0.463 (0.874)      Loss 6.3013 (6.1812)    [email protected] 68.359 (79.297)  [email protected] 91.797 (94.694)
Test: [110/196] Time 0.892 (0.868)      Loss 6.1719 (6.1863)    [email protected] 77.734 (78.790)  [email protected] 93.750 (94.457)
Test: [120/196] Time 0.162 (0.860)      Loss 6.2912 (6.1894)    [email protected] 69.922 (78.483)  [email protected] 87.500 (94.150)
Test: [130/196] Time 1.983 (0.862)      Loss 6.1764 (6.1982)    [email protected] 80.469 (77.612)  [email protected] 96.484 (93.831)
Test: [140/196] Time 0.163 (0.850)      Loss 6.2354 (6.2017)    [email protected] 74.609 (77.263)  [email protected] 91.797 (93.628)
Test: [150/196] Time 1.820 (0.845)      Loss 6.1851 (6.2053)    [email protected] 75.781 (76.930)  [email protected] 90.234 (93.385)
Test: [160/196] Time 0.166 (0.835)      Loss 6.1462 (6.2080)    [email protected] 86.328 (76.672)  [email protected] 94.141 (93.180)
Test: [170/196] Time 2.107 (0.836)      Loss 6.1428 (6.2130)    [email protected] 82.422 (76.263)  [email protected] 97.656 (93.012)
Test: [180/196] Time 0.863 (0.828)      Loss 6.3378 (6.2168)    [email protected] 69.531 (75.930)  [email protected] 92.969 (92.872)
Test: [190/196] Time 1.622 (0.827)      Loss 6.3392 (6.2167)    [email protected] 67.188 (75.920)  [email protected] 94.922 (92.899)
 * [email protected] 76.022 [email protected] 92.934
3. python darknet2caffe.py cfg/resnet50.cfg resnet50.weights resnet50-darknet2caffe.prototxt resnet50-darknet2caffe.caffemodel
4. python main.py -a resnet50-darknet2caffe --pretrained -e /home/xiaohang/ImageNet/ 
=> using pre-trained model 'resnet50-darknet2caffe'
load weights resnet50-darknet2caffe.caffemodel
Loading caffemodel:  resnet50-darknet2caffe.caffemodel
Test: [0/196]   Time 14.646 (14.646)    Loss 0.4863 (0.4863)    [email protected] 85.938 (85.938)  [email protected] 97.656 (97.656)
Test: [10/196]  Time 0.395 (1.705)      Loss 0.9623 (0.6718)    [email protected] 76.562 (82.919)  [email protected] 93.359 (95.561)
Test: [20/196]  Time 0.343 (1.213)      Loss 0.7586 (0.6859)    [email protected] 86.328 (82.738)  [email protected] 92.578 (95.424)
Test: [30/196]  Time 0.156 (1.095)      Loss 0.7881 (0.6409)    [email protected] 80.469 (84.073)  [email protected] 95.312 (95.804)
Test: [40/196]  Time 0.159 (0.989)      Loss 0.6530 (0.6863)    [email protected] 82.812 (82.336)  [email protected] 96.484 (95.798)
Test: [50/196]  Time 0.155 (0.959)      Loss 0.4764 (0.6844)    [email protected] 89.062 (82.207)  [email protected] 97.266 (95.910)
Test: [60/196]  Time 0.156 (0.921)      Loss 0.9198 (0.6984)    [email protected] 76.172 (81.807)  [email protected] 95.312 (95.959)
Test: [70/196]  Time 0.263 (0.911)      Loss 0.7103 (0.6851)    [email protected] 78.516 (82.042)  [email protected] 96.094 (96.072)
Test: [80/196]  Time 0.390 (0.887)      Loss 1.4683 (0.7112)    [email protected] 62.109 (81.520)  [email protected] 88.672 (95.737)
Test: [90/196]  Time 0.727 (0.887)      Loss 1.8474 (0.7593)    [email protected] 57.031 (80.460)  [email protected] 86.719 (95.261)
Test: [100/196] Time 0.160 (0.860)      Loss 1.1329 (0.8115)    [email protected] 68.359 (79.297)  [email protected] 91.797 (94.694)
Test: [110/196] Time 0.155 (0.857)      Loss 0.8606 (0.8358)    [email protected] 77.734 (78.790)  [email protected] 93.750 (94.457)
Test: [120/196] Time 0.301 (0.850)      Loss 1.2403 (0.8538)    [email protected] 69.922 (78.483)  [email protected] 87.500 (94.150)
Test: [130/196] Time 1.884 (0.850)      Loss 0.7038 (0.8877)    [email protected] 80.469 (77.612)  [email protected] 96.484 (93.831)
Test: [140/196] Time 0.155 (0.836)      Loss 1.0392 (0.9057)    [email protected] 74.609 (77.263)  [email protected] 91.797 (93.628)
Test: [150/196] Time 2.057 (0.835)      Loss 1.0445 (0.9234)    [email protected] 75.781 (76.930)  [email protected] 90.234 (93.385)
Test: [160/196] Time 0.157 (0.825)      Loss 0.6993 (0.9374)    [email protected] 86.328 (76.672)  [email protected] 94.141 (93.180)
Test: [170/196] Time 1.769 (0.826)      Loss 0.6132 (0.9542)    [email protected] 82.422 (76.263)  [email protected] 97.656 (93.012)
Test: [180/196] Time 0.995 (0.818)      Loss 1.2884 (0.9700)    [email protected] 69.531 (75.930)  [email protected] 92.969 (92.872)
Test: [190/196] Time 1.447 (0.815)      Loss 1.1864 (0.9686)    [email protected] 67.188 (75.920)  [email protected] 94.922 (92.899)
 * [email protected] 76.022 [email protected] 92.934

Convert yolo2 model to caffe

convert tiny-yolo from darknet to caffe

1. download tiny-yolo-voc.weights : https://pjreddie.com/media/files/tiny-yolo-voc.weights
https://github.com/pjreddie/darknet/blob/master/cfg/tiny-yolo-voc.cfg
2. python darknet2caffe.py tiny-yolo-voc.cfg tiny-yolo-voc.weights tiny-yolo-voc.prototxt tiny-yolo-voc.caffemodel
3. download voc data and process according to https://github.com/marvis/pytorch-yolo2
python valid.py cfg/voc.data tiny-yolo-voc.prototxt tiny-yolo-voc.caffemodel
4. python scripts/voc_eval.py results/comp4_det_test_
VOC07 metric? Yes
AP for aeroplane = 0.6094
AP for bicycle = 0.6781
AP for bird = 0.4573
AP for boat = 0.3786
AP for bottle = 0.2081
AP for bus = 0.6645
AP for car = 0.6587
AP for cat = 0.6720
AP for chair = 0.3245
AP for cow = 0.4902
AP for diningtable = 0.5549
AP for dog = 0.5905
AP for horse = 0.6871
AP for motorbike = 0.6695
AP for person = 0.5833
AP for pottedplant = 0.2535
AP for sheep = 0.5374
AP for sofa = 0.4878
AP for train = 0.7004
AP for tvmonitor = 0.5754
Mean AP = 0.5391
5. python detect.py tiny-yolo-voc.prototxt tiny-yolo-voc.caffemodel data/dog.jpg

convert tiny-yolo from darknet to caffe without bn

1. python darknet.py tiny-yolo-voc.cfg tiny-yolo-voc.weights tiny-yolo-voc-nobn.cfg tiny-yolo-voc-nobn.weights
2. python darknet2caffe.py tiny-yolo-voc-nobn.cfg tiny-yolo-voc-nobn.weights tiny-yolo-voc-nobn.prototxt tiny-yolo-voc-nobn.caffemodel
3. python valid.py cfg/voc.data tiny-yolo-voc-nobn.prototxt tiny-yolo-voc-nobn.caffemodel
4. python scripts/voc_eval.py results/comp4_det_test_
VOC07 metric? Yes
AP for aeroplane = 0.6094
AP for bicycle = 0.6781
AP for bird = 0.4573
AP for boat = 0.3786
AP for bottle = 0.2081
AP for bus = 0.6645
AP for car = 0.6587
AP for cat = 0.6720
AP for chair = 0.3245
AP for cow = 0.4902
AP for diningtable = 0.5549
AP for dog = 0.5905
AP for horse = 0.6871
AP for motorbike = 0.6695
AP for person = 0.5833
AP for pottedplant = 0.2535
AP for sheep = 0.5374
AP for sofa = 0.4878
AP for train = 0.7004
AP for tvmonitor = 0.5754
Mean AP = 0.5391
5. python detect.py tiny-yolo-voc-nobn.prototxt tiny-yolo-voc-nobn.caffemodel data/dog.jpg

License

MIT License (see LICENSE file).

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