From d8bafc728478e5cba9cf41eca01d66a38800eddd Mon Sep 17 00:00:00 2001
From: Alexey <AlexeyAB@users.noreply.github.com>
Date: Fri, 28 Apr 2017 11:04:56 +0000
Subject: [PATCH] Update Readme.md
---
README.md | 27 +++++++++++++++++----------
1 files changed, 17 insertions(+), 10 deletions(-)
diff --git a/README.md b/README.md
index c8af82c..85b25d5 100644
--- a/README.md
+++ b/README.md
@@ -12,7 +12,7 @@
|  |  https://arxiv.org/abs/1612.08242 |
|---|---|
-|  |  https://arxiv.org/abs/1612.08242 |
+|  |  https://arxiv.org/abs/1612.08242 |
|---|---|
@@ -26,7 +26,7 @@
More details: http://pjreddie.com/darknet/yolo/
##### Requires:
-* **MS Visual Studio 2015 (v140)**: https://www.microsoft.com/download/details.aspx?id=48146
+* **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 for Windows x64**: https://developer.nvidia.com/cuda-downloads
* **OpenCV 2.4.9**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.9/opencv-2.4.9.exe/download
- To compile without OpenCV - remove define OPENCV from: Visual Studio->Project->Properties->C/C++->Preprocessor
@@ -112,7 +112,7 @@
5. If you want to build with CUDNN to speed up then:
- * download and install CUDNN: https://developer.nvidia.com/cudnn
+ * download and install **cuDNN 5.1 for CUDA 8.0**: https://developer.nvidia.com/cudnn
* add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg
@@ -136,13 +136,14 @@
`..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cudnn.lib;%(AdditionalDependencies)`
- (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions
+`OPENCV;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;GPU;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)`
+
- open file: `\src\yolo.c` and change 3 lines to your OpenCV-version - `249` (for 2.4.9), `2413` (for 2.4.13), ... :
* `#pragma comment(lib, "opencv_core249.lib")`
* `#pragma comment(lib, "opencv_imgproc249.lib")`
* `#pragma comment(lib, "opencv_highgui249.lib")`
-`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:
`pthreadVC2.dll, pthreadGC2.dll` from \3rdparty\dll\x64
@@ -168,7 +169,9 @@
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`
+6. Set `batch=64` and `subdivisions=8` in the file `yolo-voc.cfg`: [link](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.cfg#L3)
+
+7. 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`
@@ -184,10 +187,12 @@
## 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:
+1. Create file `yolo-obj.cfg` with the same content as in `yolo-voc.2.0.cfg` (or copy `yolo-voc.2.0.cfg` to `yolo-obj.cfg)` and:
+ * change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.cfg#L3)
+ * change line subdivisions to [`subdivisions=8`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.cfg#L4)
* change line `classes=20` to your number of objects
- * change line #224 from [`filters=125`](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.cfg#L224) to `filters=(classes + 5)*5` (generally this depends on the `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`)
+ * change line #237 from [`filters=125`](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.cfg#L237) 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:
@@ -262,7 +267,7 @@
* **9002** - iteration number (number of batch)
* **0.060730 avg** - average loss (error) - **the lower, the better**
- When you see that average loss **0.060730 avg** enough low at many iterations and no longer decreases then you should stop training.
+ When you see that average loss **0.xxxxxx avg** no longer decreases at many iterations then you should stop training.
2. Once training is stopped, you should take some of last `.weights`-files from `darknet\build\darknet\x64\backup` and choose the best of them:
@@ -270,7 +275,7 @@
- 2.1. At first, you should put filenames of validation images to file `data\voc.2007.test` (format as in `train.txt`) or if you haven't validation images - simply copy `data\train.txt` to `data\voc.2007.test`.
+ 2.1. At first, in your file `obj.data` you must specify the path to the validation dataset `valid = valid.txt` (format of `valid.txt` as in `train.txt`), and if you haven't validation images, just copy `data\train.txt` to `data\valid.txt`.
2.2 If training is stopped after 9000 iterations, to validate some of previous weights use this commands:
@@ -283,7 +288,7 @@
> 7586 7612 7689 RPs/Img: 68.23 **IOU: 77.86%** Recall:99.00%
* **IOU** - the bigger, the better (says about accuracy) - **better to use**
-* **Recall** - the bigger, the better (says about accuracy)
+* **Recall** - the bigger, the better (says about accuracy) - actually Yolo calculates true positives, so it shouldn't be used
For example, **bigger IUO** gives weights `yolo-obj_8000.weights` - then **use this weights for detection**.
@@ -301,6 +306,8 @@
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/47409529d0eb935fa7bafbe2b3484431117269f5/cfg/yolo-voc.cfg#L244)
+
+ * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides
2. After training - for detection:
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