From ed023897e0d51e7ed90222118b22374e21e81dfa Mon Sep 17 00:00:00 2001 From: Alexey <AlexeyAB@users.noreply.github.com> Date: Tue, 13 Dec 2016 18:13:18 +0000 Subject: [PATCH] How to train with multi-GPU --- README.md | 29 ++++++++++++++++++++++++++++- 1 files changed, 28 insertions(+), 1 deletions(-) diff --git a/README.md b/README.md index 0c09b98..d0eb153 100644 --- a/README.md +++ b/README.md @@ -41,6 +41,7 @@ * `darknet_voc.cmd` - initialization with 256 MB VOC-model yolo-voc.weights & yolo-voc.cfg and waiting for entering the name of the image file * `darknet_demo_voc.cmd` - initialization with 256 MB VOC-model yolo-voc.weights & yolo-voc.cfg and play your video file which you must rename to: test.mp4, and store result to: test_dnn_out.avi * `darknet_net_cam_voc.cmd` - initialization with 256 MB VOC-model, play video from network video-camera mjpeg-stream (also from you phone) and store result to: test_dnn_out.avi +* `darknet_web_cam_voc.cmd` - initialization with 256 MB VOC-model, play video from Web-Camera number #0 and store result to: test_dnn_out.avi ##### How to use on the command line: * 256 MB COCO-model - image: `darknet.exe detector test data/coco.data yolo.cfg yolo.weights -i 0 -thresh 0.2` @@ -52,6 +53,7 @@ * 60 MB VOC-model for video: `darknet.exe detector demo data/voc.data tiny-yolo-voc.cfg tiny-yolo-voc.weights test.mp4 -i 0` * 256 MB COCO-model for net-videocam - Smart WebCam: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` * 256 MB VOC-model for net-videocam - Smart WebCam: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` +* 256 MB VOC-model - WebCamera #0: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights -c 0` ##### For using network video-camera mjpeg-stream with any Android smartphone: @@ -103,10 +105,35 @@ - (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions `OPENCV;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)` -- compile to .exe (X64 & Release) and put .dll`s near with .exe: +- compile to .exe (X64 & Release) and put .dll-s near with .exe: `pthreadVC2.dll, pthreadGC2.dll` from \3rdparty\dll\x64 `cusolver64_80.dll, curand64_80.dll, cudart64_80.dll, cublas64_80.dll` - 80 for CUDA 8.0 or your version, from C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\bin +## How to train (Pascal VOC Data): + +1. Download pre-trained weights for the convolutional layers (76 MB): http://pjreddie.com/media/files/darknet19_448.conv.23 and put to the directory `build\darknet\x64` + +2. Download The Pascal VOC Data and unpack it to directory `build\darknet\x64\data\voc`: http://pjreddie.com/projects/pascal-voc-dataset-mirror/ will be created file `voc_label.py` and `\VOCdevkit\` dir + +3. Download and install Python for Windows: https://www.python.org/ftp/python/3.5.2/python-3.5.2-amd64.exe + +4. Run command: `python build\darknet\x64\data\voc\voc_label.py` (to generate files: 2007_test.txt, 2007_train.txt, 2007_val.txt, 2012_train.txt, 2012_val.txt) + +5. Run command: `type 2007_train.txt 2007_val.txt 2012_*.txt > train.txt` + +6. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/voc.data yolo-voc.cfg darknet19_448.conv.23` + +If required change pathes in the file `build\darknet\x64\data\voc.data` + +More information about training by the link: http://pjreddie.com/darknet/yolo/#train-voc + +## How to train with multi-GPU: + +1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data yolo-voc.cfg darknet19_448.conv.23` + +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.cfg yolo-voc_1000.weights -gpus 0,1,2,3` + +https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ -- Gitblit v1.10.0