From 45e03601d1dd33cea959638d343af7252b0cd347 Mon Sep 17 00:00:00 2001
From: Alexey <AlexeyAB@users.noreply.github.com>
Date: Sat, 17 Dec 2016 10:56:46 +0000
Subject: [PATCH] Update Readme.md - training for custom objects

---
 README.md |   86 ++++++++++++++++++++++++++++++++++++++++++
 1 files changed, 85 insertions(+), 1 deletions(-)

diff --git a/README.md b/README.md
index 379d8fa..794829b 100644
--- a/README.md
+++ b/README.md
@@ -105,10 +105,94 @@
 - (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
+
+## How to train (to detect your custom objects):
+
+1. Create file `yolo-obj.cfg` with the same content as in `yolo-voc.cfg` (or copy `yolo-voc.cfg` to `yolo-obj.cfg)` and:
+
+  * change line `classes=20` to your number of objects
+  * change line `filters=425` to `filters=(classes + 5)*5` (generally this depends on the `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`)
+
+  For example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolo-voc.cfg` in such lines:
+
+  ```
+  [convolutional]
+  filters=35
+
+  [region]
+  classes=2
+  ```
+
+2. Create file `obj.names` in the directory `build\darknet\x64\data\`, with objects names - each in new line
+
+3. Create file `obj.data` in the directory `build\darknet\x64\data\`, containing (where **classes = number of objects**):
+
+  ```
+  classes= 2
+  train  = train.txt
+  valid  = test.txt
+  names = obj.names
+  backup = backup/
+  ```
+
+4. Put image-files (.jpg) of your objects in the directory `build\darknet\x64\data\obj\`
+
+5. Create `.txt`-file for each `.jpg`-image-file - with the same name, but with `.txt`-extension, and put to file: object number and object coordinates on this image, for each object in new line: `<object-class> <x> <y> <width> <height>`
+
+  Where: 
+  * `<object-class>` - integer number of object from `0` to `(classes-1)`
+  * `<x> <y> <width> <height>` - float values relative to width and height of image, it can be equal from 0.0 to 1.0
+  * atention: `<x> <y>` - are center of rectangle (are not top-left corner)
+
+  For example for `img1.jpg` you should create `img1.txt` containing:
+
+  ```
+  1 0.716797 0.395833 0.216406 0.147222
+  0 0.687109 0.379167 0.255469 0.158333
+  1 0.420312 0.395833 0.140625 0.166667
+  ```
+
+6. Create file `train.txt` in directory `build\darknet\x64\data\`, with filenames of your images, each filename in new line, with path relative to `darknet.exe`, for example containing:
+
+  ```
+  data/obj/img1.jpg
+  data/obj/img2.jpg
+  data/obj/img3.jpg
+  ```
+
+7. 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`
+
+8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23`
+

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