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| | | # Yolo-v2 Windows and Linux version |
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| | | [](https://circleci.com/gh/AlexeyAB/darknet) |
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| | | 1. [How to use](#how-to-use) |
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| | | 2. [How to compile on Linux](#how-to-compile-on-linux) |
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| | | 3. [How to compile on Windows](#how-to-compile-on-windows) |
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| | | * 194 MB VOC-model - WebCamera #0: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights -c 0` |
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| | | * 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data yolo9000.cfg yolo9000.weights` |
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| | | * 186 MB Yolo9000 - video: `darknet.exe detector demo cfg/combine9k.data yolo9000.cfg yolo9000.weights test.mp4` |
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| | | * To process a list of images `image_list.txt` and save results of detection to `result.txt` use: |
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| | | `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights < image_list.txt > result.txt` |
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| | | You can comment this line so that each image does not require pressing the button ESC: https://github.com/AlexeyAB/darknet/blob/6ccb41808caf753feea58ca9df79d6367dedc434/src/detector.c#L509 |
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| | | ##### For using network video-camera mjpeg-stream with any Android smartphone: |
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| | | * `OPENCV=1` to build with OpenCV 3.x/2.4.x - allows to detect on video files and video streams from network cameras or web-cams |
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| | | * `DEBUG=1` to bould debug version of Yolo |
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| | | * `OPENMP=1` to build with OpenMP support to accelerate Yolo by using multi-core CPU |
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| | | * `LIBSO=1` to build a library `darknet.so` and binary runable file `uselib` that uses this library. How to use this SO-library from your own code - you can look at C++ example: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp |
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| | | * `LIBSO=1` to build a library `darknet.so` and binary runable file `uselib` that uses this library. Or you can try to run so `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib test.mp4` How to use this SO-library from your own code - you can look at C++ example: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp |
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| | | ### How to compile on Windows: |
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| | | 1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data yolo-voc.2.0.cfg darknet19_448.conv.23` |
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| | | 2. Then stop and by using partially-trained model `/backup/yolo-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data yolo-voc.2.0.cfg yolo-voc_1000.weights -gpus 0,1,2,3` |
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| | | 2. Then stop and by using partially-trained model `/backup/yolo-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data yolo-voc.2.0.cfg /backup/yolo-voc_1000.weights -gpus 0,1,2,3` |
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| | | https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ |
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