From 823c3f4b288be5bb5bc9b6f3bf41e809e66b6279 Mon Sep 17 00:00:00 2001 From: AlexeyAB <alexeyab84@gmail.com> Date: Mon, 16 Apr 2018 13:01:35 +0000 Subject: [PATCH] Fixed AVX detection on Linux. --- README.md | 14 ++++++++++---- 1 files changed, 10 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 55c36a5..926bcde 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ * **OpenCV 3.4.0**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.4.0/opencv-3.4.0-vc14_vc15.exe/download * **or OpenCV 2.4.13**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.13/opencv-2.4.13.2-vc14.exe/download - OpenCV allows to show image or video detection in the window and store result to file that specified in command line `-out_filename res.avi` -* **GPU with CC >= 2.0** if you use CUDA, or **GPU CC >= 3.0** if you use cuDNN + CUDA: https://en.wikipedia.org/wiki/CUDA#GPUs_supported +* **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported ##### Pre-trained models for different cfg-files can be downloaded from (smaller -> faster & lower quality): * `yolov3.cfg` (236 MB COCO **Yolo v3**) - require 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3.weights @@ -167,7 +167,7 @@ `C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include` - (right click on project) -> Build dependecies -> Build Customizations -> set check on CUDA 9.1 or what version you have - for example as here: http://devblogs.nvidia.com/parallelforall/wp-content/uploads/2015/01/VS2013-R-5.jpg -- add to project all .c & .cu files from `\src` +- add to project all `.c` & `.cu` files and file `http_stream.cpp` from `\src` - (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here: `C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories)` @@ -216,6 +216,8 @@ More information about training by the link: http://pjreddie.com/darknet/yolo/#train-voc + **Note:** If during training you see `nan` values in some lines then training goes well, but if `nan` are in all lines then training goes wrong. + ## How to train with multi-GPU: 1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg darknet53.conv.74` @@ -225,7 +227,9 @@ https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ ## How to train (to detect your custom objects): -Training Yolo v3 +(to train old Yolo v2 `yolov2-voc.cfg`, `yolov2-tiny-voc.cfg`, `yolo-voc.cfg`, `yolo-voc.2.0.cfg`, ... [click by the link](https://github.com/AlexeyAB/darknet/tree/47c7af1cea5bbdedf1184963355e6418cb8b1b4f#how-to-train-pascal-voc-data)) + +Training Yolo v3: 1. Create file `yolo-obj.cfg` with the same content as in `yolov3.cfg` (or copy `yolov3.cfg` to `yolo-obj.cfg)` and: @@ -305,7 +309,9 @@ 9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\` - * After each 1000 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just copy `yolo-obj_2000.weights` from `build\darknet\x64\backup\` to `build\darknet\x64\` and start training using: `darknet.exe detector train data/obj.data yolo-obj.cfg yolo-obj_2000.weights` + * After each 100 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just copy `yolo-obj_2000.weights` from `build\darknet\x64\backup\` to `build\darknet\x64\` and start training using: `darknet.exe detector train data/obj.data yolo-obj.cfg yolo-obj_2000.weights` + + (in the original repository https://github.com/pjreddie/darknet the weights-file is saved only once every 10 000 iterations `if(iterations > 1000)`) * Also you can get result earlier than all 45000 iterations. -- Gitblit v1.10.0