From bcdf943ca780f17758df49f2df2cc138eec9ad75 Mon Sep 17 00:00:00 2001
From: Jud White <github@judsonwhite.com>
Date: Sun, 25 Mar 2018 20:51:48 +0000
Subject: [PATCH] reversed order
---
README.md | 56 +++++++++++++++++++++++++++++++++++++-------------------
1 files changed, 37 insertions(+), 19 deletions(-)
diff --git a/README.md b/README.md
index ec8c19a..18e5ab3 100644
--- a/README.md
+++ b/README.md
@@ -31,15 +31,15 @@
This repository supports:
* both Windows and Linux
-* both OpenCV 3.x and OpenCV 2.4.13
-* both cuDNN 5 and cuDNN 6
+* both OpenCV 2.x.x and OpenCV <= 3.4.0 (3.4.1 and higher isn't supported)
+* both cuDNN v5-v7
* CUDA >= 7.5
* also create SO-library on Linux and DLL-library on Windows
##### Requires:
* **Linux GCC>=4.9 or Windows MS Visual Studio 2015 (v140)**: https://go.microsoft.com/fwlink/?LinkId=532606&clcid=0x409 (or offline [ISO image](https://go.microsoft.com/fwlink/?LinkId=615448&clcid=0x409))
-* **CUDA 8.0**: https://developer.nvidia.com/cuda-downloads
-* **OpenCV 3.x**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.2.0/opencv-3.2.0-vc14.exe/download
+* **CUDA 9.1**: https://developer.nvidia.com/cuda-downloads
+* **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
@@ -92,8 +92,8 @@
* 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data yolo9000.cfg yolo9000.weights`
* 186 MB Yolo9000 - video: `darknet.exe detector demo cfg/combine9k.data yolo9000.cfg yolo9000.weights test.mp4`
* Remeber to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app
-* To process a list of images `image_list.txt` and save results of detection to `result.txt` use:
- `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights < image_list.txt > result.txt`
+* To process a list of images `data/train.txt` and save results of detection to `result.txt` use:
+ `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -dont_show < data/train.txt > result.txt`
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
##### For using network video-camera mjpeg-stream with any Android smartphone:
@@ -117,7 +117,7 @@
Just do `make` in the darknet directory.
Before make, you can set such options in the `Makefile`: [link](https://github.com/AlexeyAB/darknet/blob/9c1b9a2cf6363546c152251be578a21f3c3caec6/Makefile#L1)
* `GPU=1` to build with CUDA to accelerate by using GPU (CUDA should be in `/usr/local/cuda`)
-* `CUDNN=1` to build with cuDNN v5/v6 to accelerate training by using GPU (cuDNN should be in `/usr/local/cudnn`)
+* `CUDNN=1` to build with cuDNN v5-v7 to accelerate training by using GPU (cuDNN should be in `/usr/local/cudnn`)
* `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
* `DEBUG=1` to bould debug version of Yolo
* `OPENMP=1` to build with OpenMP support to accelerate Yolo by using multi-core CPU
@@ -126,11 +126,11 @@
### How to compile on Windows:
-1. If you have **MSVS 2015, CUDA 8.0 and OpenCV 3.0** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release**, and do the: Build -> Build darknet
+1. If you have **MSVS 2015, CUDA 9.1 and OpenCV 3.0** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release**, and do the: Build -> Build darknet. **NOTE:** If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see [#500](https://github.com/AlexeyAB/darknet/issues/500)).
- 1.1. Find files `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` in `C:\opencv_3.0\opencv\build\x64\vc14\bin` and put it near with `darknet.exe`
+ 1.1. Find files `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` (or `opencv_world340.dll` and `opencv_ffmpeg340_64.dll`) in `C:\opencv_3.0\opencv\build\x64\vc14\bin` and put it near with `darknet.exe`
-2. If you have other version of **CUDA (not 8.0)** then open `build\darknet\darknet.vcxproj` by using Notepad, find 2 places with "CUDA 8.0" and change it to your CUDA-version, then do step 1
+2. If you have other version of **CUDA (not 9.1)** then open `build\darknet\darknet.vcxproj` by using Notepad, find 2 places with "CUDA 9.1" and change it to your CUDA-version, then do step 1
3. If you **don't have GPU**, but have **MSVS 2015 and OpenCV 3.0** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet_no_gpu.sln`, set **x64** and **Release**, and do the: Build -> Build darknet_no_gpu
@@ -142,7 +142,9 @@
5. If you want to build with CUDNN to speed up then:
- * download and install **cuDNN 6.0 for CUDA 8.0**: https://developer.nvidia.com/cudnn
+ * download and install **cuDNN 7.0 for CUDA 9.1**: https://developer.nvidia.com/cudnn
+
+ * copy the `bin` and `include` folders to `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1`
* add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg
@@ -150,13 +152,13 @@
### How to compile (custom):
-Also, you can to create your own `darknet.sln` & `darknet.vcxproj`, this example for CUDA 8.0 and OpenCV 3.0
+Also, you can to create your own `darknet.sln` & `darknet.vcxproj`, this example for CUDA 9.1 and OpenCV 3.0
Then add to your created project:
- (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories, put here:
`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 8.0 or what version you have - for example as here: http://devblogs.nvidia.com/parallelforall/wp-content/uploads/2015/01/VS2013-R-5.jpg
+- (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`
- (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here:
@@ -172,9 +174,9 @@
* `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
+ * `cusolver64_91.dll, curand64_91.dll, cudart64_91.dll, cublas64_91.dll` - 91 for CUDA 9.1 or your version, from C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1\bin
- * For OpenCV 3.X: `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` from `C:\opencv_3.0\opencv\build\x64\vc14\bin`
+ * For OpenCV 3.2: `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` from `C:\opencv_3.0\opencv\build\x64\vc14\bin`
* For OpenCV 2.4.13: `opencv_core2413.dll`, `opencv_highgui2413.dll` and `opencv_ffmpeg2413_64.dll` from `C:\opencv_2.4.13\opencv\build\x64\vc14\bin`
## How to train (Pascal VOC Data):
@@ -196,7 +198,7 @@
6. Set `batch=64` and `subdivisions=8` in the file `yolo-voc.2.0.cfg`: [link](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.2.0.cfg#L2)
-7. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/voc.data yolo-voc.2.0.cfg darknet19_448.conv.23` (**Note:** If you are using CPU, try `darknet_no_gpu.exe` instead of `darknet.exe`.)
+7. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/voc.data yolo-voc.2.0.cfg darknet19_448.conv.23` (**Note:** To disable Loss-Window use flag `-dont_show`. If you are using CPU, try `darknet_no_gpu.exe` instead of `darknet.exe`.)
If required change pathes in the file `build\darknet\x64\data\voc.data`
@@ -274,6 +276,7 @@
8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23`
(file `yolo-obj_xxx.weights` will be saved to the `build\darknet\x64\backup\` for each 100 iterations)
+ (To disable Loss-Window use `darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23 -dont_show`, if you train on computer without monitor like a cloud Amazaon EC2)
9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\`
@@ -281,6 +284,17 @@
* Also you can get result earlier than all 45000 iterations.
+### How to train tiny-yolo (to detect your custom objects):
+
+Do all the same steps as for the full yolo model as described above. With the exception of:
+* Download default weights file for tiny-yolo-voc: http://pjreddie.com/media/files/tiny-yolo-voc.weights
+* Get pre-trained weights tiny-yolo-voc.conv.13 using command: `darknet.exe partial cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights tiny-yolo-voc.conv.13 13`
+* Make your custom model `tiny-yolo-obj.cfg` based on `tiny-yolo-voc.cfg` instead of `yolo-voc.2.0.cfg`
+* Start training: `darknet.exe detector train data/obj.data tiny-yolo-obj.cfg tiny-yolo-voc.conv.13`
+
+For training Yolo based on other models ([DenseNet201-Yolo](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/densenet201_yolo.cfg) or [ResNet50-Yolo](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/resnet50_yolo.cfg)), you can download and get pre-trained weights as showed in this file: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/partial.cmd
+If you made you custom model that isn't based on other models, then you can train it without pre-trained weights, then will be used random initial weights.
+
## When should I stop training:
Usually sufficient 2000 iterations for each class(object). But for a more precise definition when you should stop training, use the following manual:
@@ -358,12 +372,16 @@
1. Before training:
* set flag `random=1` in your `.cfg`-file - it will increase precision by training Yolo for different resolutions: [link]https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.2.0.cfg#L244)
-
+
+ * increase network resolution in your `.cfg`-file (`height=608`, `width=608` or any value multiple of 32) - it will increase precision
+
* desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides
- * for training on small objects, add the parameter `small_object=1` in the last layer [region] in your cfg-file
+ * desirable that your training dataset include images with objects (without labels) that you do not want to detect - negative samples
* for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last layer [region] in your cfg-file
+
+ * to speedup training (with decreasing detection accuracy) do Fine-Tuning instead of Transfer-Learning, set param `stopbackward=1` in one of the penultimate convolutional layers, for example here: https://github.com/AlexeyAB/darknet/blob/cad4d1618fee74471d335314cb77070fee951a42/cfg/yolo-voc.2.0.cfg#L202
2. After training - for detection:
@@ -403,7 +421,7 @@
## How to use Yolo as DLL
1. To compile Yolo as C++ DLL-file `yolo_cpp_dll.dll` - open in MSVS2015 file `build\darknet\yolo_cpp_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_cpp_dll
- * You should have installed **CUDA 8.0**
+ * You should have installed **CUDA 9.1**
* To use cuDNN do: (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add at the beginning of line: `CUDNN;`
2. To use Yolo as DLL-file in your C++ console application - open in MSVS2015 file `build\darknet\yolo_console_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_console_dll
--
Gitblit v1.10.0