WARNING: Move forward to ysh329/darknet2caffe: Convert Darknet model to Caffe's

darknet-to-caffe-model-convertor

This repository forked from original is used to support conversion from darkent to caffe, especially for YOLOv2 and tiny-YOLO etc. Before use, ensure caffe installed, recommanding using Docker image of bvlc/caffe:cpu instead.

After that, if your model is YOLOv2 or having reorg layer, you should define the output dimension of reorg layer in code darknet2caffe.py as below:

            # TODO: auto shape infer
            shape['dim'] = [1, 2048, 9, 9]

If do not sure the output dimension of reorg layer, execute model again using darknet and check its execution log, which clearly shows the output dimension of reorg layer.

After definination of shape['dim'] variable, use command below to convert darknet model to caffe's:

python darknet2caffe.py DARKNET_CFG DARKNET_WEIGHTS CAFFE_PROTOTOXT CAFFE_CAFFEMODEL

Below is from original, which can be ignored.

---

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:

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)    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).