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.
- darknet2pytorch : use darknet.py to load darknet model directly
- caffe2pytorch : use caffenet.py to load caffe model directly, furthur supports moved to caffe2pytorch
- darknet2caffe
- caffe2darknet
- pytorch2caffe
- pytorch2darknet : pytorch2caffe then caffe2darknet
- 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) Prec@1 85.938 (85.938) Prec@5 97.656 (97.656)
Test: [10/196] Time 0.179 (1.616) Loss 0.9623 (0.6718) Prec@1 76.562 (82.919) Prec@5 93.359 (95.561)
Test: [20/196] Time 0.165 (1.152) Loss 0.7586 (0.6859) Prec@1 86.328 (82.738) Prec@5 92.578 (95.424)
Test: [30/196] Time 0.253 (1.061) Loss 0.7881 (0.6409) Prec@1 80.469 (84.073) Prec@5 95.312 (95.804)
Test: [40/196] Time 0.648 (0.973) Loss 0.6530 (0.6863) Prec@1 82.812 (82.336) Prec@5 96.484 (95.798)
Test: [50/196] Time 0.153 (0.938) Loss 0.4764 (0.6844) Prec@1 89.062 (82.207) Prec@5 97.266 (95.910)
Test: [60/196] Time 0.149 (0.908) Loss 0.9198 (0.6984) Prec@1 76.172 (81.807) Prec@5 95.312 (95.959)
Test: [70/196] Time 0.645 (0.903) Loss 0.7103 (0.6851) Prec@1 78.516 (82.042) Prec@5 96.094 (96.072)
Test: [80/196] Time 0.663 (0.884) Loss 1.4683 (0.7112) Prec@1 62.109 (81.520) Prec@5 88.672 (95.737)
Test: [90/196] Time 1.429 (0.881) Loss 1.8474 (0.7593) Prec@1 57.031 (80.460) Prec@5 86.719 (95.261)
Test: [100/196] Time 0.195 (0.859) Loss 1.1329 (0.8115) Prec@1 68.359 (79.297) Prec@5 91.797 (94.694)
Test: [110/196] Time 1.109 (0.859) Loss 0.8606 (0.8358) Prec@1 77.734 (78.790) Prec@5 93.750 (94.457)
Test: [120/196] Time 0.153 (0.851) Loss 1.2403 (0.8538) Prec@1 69.922 (78.483) Prec@5 87.500 (94.150)
Test: [130/196] Time 2.340 (0.851) Loss 0.7038 (0.8877) Prec@1 80.469 (77.612) Prec@5 96.484 (93.831)
Test: [140/196] Time 0.139 (0.839) Loss 1.0392 (0.9057) Prec@1 74.609 (77.263) Prec@5 91.797 (93.628)
Test: [150/196] Time 2.273 (0.839) Loss 1.0445 (0.9234) Prec@1 75.781 (76.930) Prec@5 90.234 (93.385)
Test: [160/196] Time 0.153 (0.830) Loss 0.6993 (0.9374) Prec@1 86.328 (76.672) Prec@5 94.141 (93.180)
Test: [170/196] Time 2.016 (0.831) Loss 0.6132 (0.9542) Prec@1 82.422 (76.263) Prec@5 97.656 (93.012)
Test: [180/196] Time 0.926 (0.823) Loss 1.2884 (0.9700) Prec@1 69.531 (75.930) Prec@5 92.969 (92.872)
Test: [190/196] Time 1.609 (0.821) Loss 1.1864 (0.9686) Prec@1 67.188 (75.920) Prec@5 94.922 (92.899)
* Prec@1 76.022 Prec@5 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) Prec@1 85.938 (85.938) Prec@5 97.656 (97.656)
Test: [10/196] Time 0.356 (1.678) Loss 0.9623 (0.6718) Prec@1 76.562 (82.919) Prec@5 93.359 (95.561)
Test: [20/196] Time 0.183 (1.206) Loss 0.7586 (0.6859) Prec@1 86.328 (82.738) Prec@5 92.578 (95.424)
Test: [30/196] Time 0.428 (1.112) Loss 0.7881 (0.6409) Prec@1 80.469 (84.073) Prec@5 95.312 (95.804)
Test: [40/196] Time 0.820 (1.022) Loss 0.6530 (0.6863) Prec@1 82.812 (82.336) Prec@5 96.484 (95.798)
Test: [50/196] Time 0.290 (0.978) Loss 0.4764 (0.6844) Prec@1 89.062 (82.207) Prec@5 97.266 (95.910)
Test: [60/196] Time 0.477 (0.941) Loss 0.9198 (0.6984) Prec@1 76.172 (81.807) Prec@5 95.312 (95.959)
Test: [70/196] Time 0.246 (0.927) Loss 0.7103 (0.6851) Prec@1 78.516 (82.042) Prec@5 96.094 (96.072)
Test: [80/196] Time 0.877 (0.910) Loss 1.4683 (0.7112) Prec@1 62.109 (81.520) Prec@5 88.672 (95.737)
Test: [90/196] Time 0.752 (0.906) Loss 1.8474 (0.7593) Prec@1 57.031 (80.460) Prec@5 86.719 (95.261)
Test: [100/196] Time 0.156 (0.883) Loss 1.1329 (0.8115) Prec@1 68.359 (79.297) Prec@5 91.797 (94.694)
Test: [110/196] Time 0.324 (0.882) Loss 0.8606 (0.8358) Prec@1 77.734 (78.790) Prec@5 93.750 (94.457)
Test: [120/196] Time 0.486 (0.878) Loss 1.2403 (0.8538) Prec@1 69.922 (78.483) Prec@5 87.500 (94.150)
Test: [130/196] Time 1.067 (0.871) Loss 0.7038 (0.8877) Prec@1 80.469 (77.612) Prec@5 96.484 (93.831)
Test: [140/196] Time 0.261 (0.863) Loss 1.0392 (0.9057) Prec@1 74.609 (77.263) Prec@5 91.797 (93.628)
Test: [150/196] Time 0.354 (0.852) Loss 1.0445 (0.9234) Prec@1 75.781 (76.930) Prec@5 90.234 (93.385)
Test: [160/196] Time 0.152 (0.851) Loss 0.6993 (0.9374) Prec@1 86.328 (76.672) Prec@5 94.141 (93.180)
Test: [170/196] Time 0.688 (0.842) Loss 0.6132 (0.9542) Prec@1 82.422 (76.263) Prec@5 97.656 (93.012)
Test: [180/196] Time 0.244 (0.839) Loss 1.2884 (0.9700) Prec@1 69.531 (75.930) Prec@5 92.969 (92.872)
Test: [190/196] Time 0.383 (0.834) Loss 1.1864 (0.9686) Prec@1 67.188 (75.920) Prec@5 94.922 (92.899)
* Prec@1 76.022 Prec@5 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) Prec@1 85.938 (85.938) Prec@5 97.656 (97.656)
Test: [10/196] Time 0.393 (1.760) Loss 0.9623 (0.6718) Prec@1 76.562 (82.919) Prec@5 93.359 (95.561)
Test: [20/196] Time 0.264 (1.241) Loss 0.7586 (0.6859) Prec@1 86.328 (82.738) Prec@5 92.578 (95.424)
Test: [30/196] Time 0.160 (1.123) Loss 0.7881 (0.6409) Prec@1 80.469 (84.073) Prec@5 95.312 (95.804)
Test: [40/196] Time 0.789 (1.020) Loss 0.6530 (0.6863) Prec@1 82.812 (82.336) Prec@5 96.484 (95.798)
Test: [50/196] Time 0.354 (0.983) Loss 0.4764 (0.6844) Prec@1 89.062 (82.207) Prec@5 97.266 (95.910)
Test: [60/196] Time 0.458 (0.946) Loss 0.9198 (0.6984) Prec@1 76.172 (81.807) Prec@5 95.312 (95.959)
Test: [70/196] Time 0.848 (0.936) Loss 0.7103 (0.6851) Prec@1 78.516 (82.042) Prec@5 96.094 (96.072)
Test: [80/196] Time 0.993 (0.918) Loss 1.4683 (0.7112) Prec@1 62.109 (81.520) Prec@5 88.672 (95.737)
Test: [90/196] Time 1.750 (0.911) Loss 1.8474 (0.7593) Prec@1 57.031 (80.460) Prec@5 86.719 (95.261)
Test: [100/196] Time 0.160 (0.889) Loss 1.1329 (0.8115) Prec@1 68.359 (79.297) Prec@5 91.797 (94.694)
Test: [110/196] Time 1.261 (0.883) Loss 0.8606 (0.8358) Prec@1 77.734 (78.790) Prec@5 93.750 (94.457)
Test: [120/196] Time 0.667 (0.874) Loss 1.2403 (0.8538) Prec@1 69.922 (78.483) Prec@5 87.500 (94.150)
Test: [130/196] Time 1.216 (0.867) Loss 0.7038 (0.8877) Prec@1 80.469 (77.612) Prec@5 96.484 (93.831)
Test: [140/196] Time 0.166 (0.857) Loss 1.0392 (0.9057) Prec@1 74.609 (77.263) Prec@5 91.797 (93.628)
Test: [150/196] Time 1.123 (0.850) Loss 1.0445 (0.9234) Prec@1 75.781 (76.930) Prec@5 90.234 (93.385)
Test: [160/196] Time 0.161 (0.845) Loss 0.6993 (0.9374) Prec@1 86.328 (76.672) Prec@5 94.141 (93.180)
Test: [170/196] Time 0.345 (0.837) Loss 0.6132 (0.9542) Prec@1 82.422 (76.263) Prec@5 97.656 (93.012)
Test: [180/196] Time 1.152 (0.839) Loss 1.2884 (0.9700) Prec@1 69.531 (75.930) Prec@5 92.969 (92.872)
Test: [190/196] Time 0.165 (0.829) Loss 1.1864 (0.9686) Prec@1 67.188 (75.920) Prec@5 94.922 (92.899)
* Prec@1 76.022 Prec@5 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) Prec@1 85.938 (85.938) Prec@5 97.656 (97.656)
Test: [10/196] Time 0.470 (3.075) Loss 0.9623 (0.6718) Prec@1 76.562 (82.919) Prec@5 93.359 (95.561)
Test: [20/196] Time 0.221 (1.940) Loss 0.7586 (0.6859) Prec@1 86.328 (82.738) Prec@5 92.578 (95.424)
Test: [30/196] Time 0.890 (1.617) Loss 0.7881 (0.6409) Prec@1 80.469 (84.073) Prec@5 95.312 (95.804)
Test: [40/196] Time 1.176 (1.426) Loss 0.6530 (0.6863) Prec@1 82.812 (82.336) Prec@5 96.484 (95.798)
Test: [50/196] Time 1.331 (1.304) Loss 0.4764 (0.6844) Prec@1 89.062 (82.207) Prec@5 97.266 (95.910)
Test: [60/196] Time 0.520 (1.223) Loss 0.9198 (0.6984) Prec@1 76.172 (81.807) Prec@5 95.312 (95.959)
Test: [70/196] Time 0.397 (1.184) Loss 0.7103 (0.6851) Prec@1 78.516 (82.042) Prec@5 96.094 (96.072)
Test: [80/196] Time 0.666 (1.141) Loss 1.4683 (0.7112) Prec@1 62.109 (81.520) Prec@5 88.672 (95.737)
Test: [90/196] Time 0.759 (1.121) Loss 1.8474 (0.7593) Prec@1 57.031 (80.460) Prec@5 86.719 (95.261)
Test: [100/196] Time 0.153 (1.082) Loss 1.1329 (0.8115) Prec@1 68.359 (79.297) Prec@5 91.797 (94.694)
Test: [110/196] Time 0.511 (1.068) Loss 0.8606 (0.8358) Prec@1 77.734 (78.790) Prec@5 93.750 (94.457)
Test: [120/196] Time 0.643 (1.057) Loss 1.2403 (0.8538) Prec@1 69.922 (78.483) Prec@5 87.500 (94.150)
Test: [130/196] Time 1.309 (1.040) Loss 0.7038 (0.8877) Prec@1 80.469 (77.612) Prec@5 96.484 (93.831)
Test: [140/196] Time 0.261 (1.021) Loss 1.0392 (0.9057) Prec@1 74.609 (77.263) Prec@5 91.797 (93.628)
Test: [150/196] Time 1.744 (1.013) Loss 1.0445 (0.9234) Prec@1 75.781 (76.930) Prec@5 90.234 (93.385)
Test: [160/196] Time 0.222 (0.997) Loss 0.6993 (0.9374) Prec@1 86.328 (76.672) Prec@5 94.141 (93.180)
Test: [170/196] Time 1.306 (0.994) Loss 0.6132 (0.9542) Prec@1 82.422 (76.263) Prec@5 97.656 (93.012)
Test: [180/196] Time 0.609 (0.978) Loss 1.2884 (0.9700) Prec@1 69.531 (75.930) Prec@5 92.969 (92.872)
Test: [190/196] Time 0.505 (0.972) Loss 1.1864 (0.9686) Prec@1 67.188 (75.920) Prec@5 94.922 (92.899)
* Prec@1 76.022 Prec@5 92.934
Note:
- imagenet data is processed as described here
- 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) Prec@1 85.938 (85.938) Prec@5 97.656 (97.656)
Test: [10/196] Time 0.380 (1.716) Loss 6.2165 (6.1346) Prec@1 76.562 (82.919) Prec@5 93.359 (95.561)
Test: [20/196] Time 0.167 (1.205) Loss 6.0981 (6.1388) Prec@1 86.328 (82.738) Prec@5 92.578 (95.424)
Test: [30/196] Time 0.163 (1.100) Loss 6.1633 (6.1244) Prec@1 80.469 (84.073) Prec@5 95.312 (95.804)
Test: [40/196] Time 0.862 (1.009) Loss 6.1777 (6.1473) Prec@1 82.812 (82.336) Prec@5 96.484 (95.798)
Test: [50/196] Time 0.713 (0.965) Loss 6.0856 (6.1510) Prec@1 89.062 (82.207) Prec@5 97.266 (95.910)
Test: [60/196] Time 0.867 (0.936) Loss 6.1982 (6.1557) Prec@1 76.172 (81.807) Prec@5 95.312 (95.959)
Test: [70/196] Time 0.451 (0.917) Loss 6.1979 (6.1513) Prec@1 78.516 (82.042) Prec@5 96.094 (96.072)
Test: [80/196] Time 1.749 (0.909) Loss 6.3671 (6.1568) Prec@1 62.109 (81.520) Prec@5 88.672 (95.737)
Test: [90/196] Time 0.904 (0.892) Loss 6.4027 (6.1684) Prec@1 57.031 (80.460) Prec@5 86.719 (95.261)
Test: [100/196] Time 0.463 (0.874) Loss 6.3013 (6.1812) Prec@1 68.359 (79.297) Prec@5 91.797 (94.694)
Test: [110/196] Time 0.892 (0.868) Loss 6.1719 (6.1863) Prec@1 77.734 (78.790) Prec@5 93.750 (94.457)
Test: [120/196] Time 0.162 (0.860) Loss 6.2912 (6.1894) Prec@1 69.922 (78.483) Prec@5 87.500 (94.150)
Test: [130/196] Time 1.983 (0.862) Loss 6.1764 (6.1982) Prec@1 80.469 (77.612) Prec@5 96.484 (93.831)
Test: [140/196] Time 0.163 (0.850) Loss 6.2354 (6.2017) Prec@1 74.609 (77.263) Prec@5 91.797 (93.628)
Test: [150/196] Time 1.820 (0.845) Loss 6.1851 (6.2053) Prec@1 75.781 (76.930) Prec@5 90.234 (93.385)
Test: [160/196] Time 0.166 (0.835) Loss 6.1462 (6.2080) Prec@1 86.328 (76.672) Prec@5 94.141 (93.180)
Test: [170/196] Time 2.107 (0.836) Loss 6.1428 (6.2130) Prec@1 82.422 (76.263) Prec@5 97.656 (93.012)
Test: [180/196] Time 0.863 (0.828) Loss 6.3378 (6.2168) Prec@1 69.531 (75.930) Prec@5 92.969 (92.872)
Test: [190/196] Time 1.622 (0.827) Loss 6.3392 (6.2167) Prec@1 67.188 (75.920) Prec@5 94.922 (92.899)
* Prec@1 76.022 Prec@5 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) Prec@1 85.938 (85.938) Prec@5 97.656 (97.656)
Test: [10/196] Time 0.395 (1.705) Loss 0.9623 (0.6718) Prec@1 76.562 (82.919) Prec@5 93.359 (95.561)
Test: [20/196] Time 0.343 (1.213) Loss 0.7586 (0.6859) Prec@1 86.328 (82.738) Prec@5 92.578 (95.424)
Test: [30/196] Time 0.156 (1.095) Loss 0.7881 (0.6409) Prec@1 80.469 (84.073) Prec@5 95.312 (95.804)
Test: [40/196] Time 0.159 (0.989) Loss 0.6530 (0.6863) Prec@1 82.812 (82.336) Prec@5 96.484 (95.798)
Test: [50/196] Time 0.155 (0.959) Loss 0.4764 (0.6844) Prec@1 89.062 (82.207) Prec@5 97.266 (95.910)
Test: [60/196] Time 0.156 (0.921) Loss 0.9198 (0.6984) Prec@1 76.172 (81.807) Prec@5 95.312 (95.959)
Test: [70/196] Time 0.263 (0.911) Loss 0.7103 (0.6851) Prec@1 78.516 (82.042) Prec@5 96.094 (96.072)
Test: [80/196] Time 0.390 (0.887) Loss 1.4683 (0.7112) Prec@1 62.109 (81.520) Prec@5 88.672 (95.737)
Test: [90/196] Time 0.727 (0.887) Loss 1.8474 (0.7593) Prec@1 57.031 (80.460) Prec@5 86.719 (95.261)
Test: [100/196] Time 0.160 (0.860) Loss 1.1329 (0.8115) Prec@1 68.359 (79.297) Prec@5 91.797 (94.694)
Test: [110/196] Time 0.155 (0.857) Loss 0.8606 (0.8358) Prec@1 77.734 (78.790) Prec@5 93.750 (94.457)
Test: [120/196] Time 0.301 (0.850) Loss 1.2403 (0.8538) Prec@1 69.922 (78.483) Prec@5 87.500 (94.150)
Test: [130/196] Time 1.884 (0.850) Loss 0.7038 (0.8877) Prec@1 80.469 (77.612) Prec@5 96.484 (93.831)
Test: [140/196] Time 0.155 (0.836) Loss 1.0392 (0.9057) Prec@1 74.609 (77.263) Prec@5 91.797 (93.628)
Test: [150/196] Time 2.057 (0.835) Loss 1.0445 (0.9234) Prec@1 75.781 (76.930) Prec@5 90.234 (93.385)
Test: [160/196] Time 0.157 (0.825) Loss 0.6993 (0.9374) Prec@1 86.328 (76.672) Prec@5 94.141 (93.180)
Test: [170/196] Time 1.769 (0.826) Loss 0.6132 (0.9542) Prec@1 82.422 (76.263) Prec@5 97.656 (93.012)
Test: [180/196] Time 0.995 (0.818) Loss 1.2884 (0.9700) Prec@1 69.531 (75.930) Prec@5 92.969 (92.872)
Test: [190/196] Time 1.447 (0.815) Loss 1.1864 (0.9686) Prec@1 67.188 (75.920) Prec@5 94.922 (92.899)
* Prec@1 76.022 Prec@5 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).