From ed56fbbc905ec4bb67fce9cf539b4a79e240fe5a Mon Sep 17 00:00:00 2001
From: Alexey <AlexeyAB@users.noreply.github.com>
Date: Thu, 16 Mar 2017 10:27:03 +0000
Subject: [PATCH] Update Readme.md - DLLs API
---
README.md | 170 +++++++++++++++++++++++++++++++++++++++++++++++++++++---
1 files changed, 159 insertions(+), 11 deletions(-)
diff --git a/README.md b/README.md
index 794829b..c8af82c 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,21 @@
-
-
# Yolo-Windows v2
+
+1. [How to use](#how-to-use)
+2. [How to compile](#how-to-compile)
+3. [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data)
+4. [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects)
+5. [When should I stop training](#when-should-i-stop-training)
+6. [How to improve object detection](#how-to-improve-object-detection)
+7. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files)
+8. [How to use Yolo as DLL](#how-to-use-yolo-as-dll)
+
+|  |  https://arxiv.org/abs/1612.08242 |
+|---|---|
+
+|  |  https://arxiv.org/abs/1612.08242 |
+|---|---|
+
+
# "You Only Look Once: Unified, Real-Time Object Detection (version 2)"
A yolo windows version (for object detection)
@@ -60,8 +75,8 @@
1. Download for Android phone mjpeg-stream soft: IP Webcam / Smart WebCam
- Smart WebCam - preferably: https://play.google.com/store/apps/details?id=com.acontech.android.SmartWebCam
- IP Webcam: https://play.google.com/store/apps/details?id=com.pas.webcam
+ * Smart WebCam - preferably: https://play.google.com/store/apps/details?id=com.acontech.android.SmartWebCam2
+ * IP Webcam: https://play.google.com/store/apps/details?id=com.pas.webcam
2. Connect your Android phone to computer by WiFi (through a WiFi-router) or USB
3. Start Smart WebCam on your phone
@@ -74,7 +89,9 @@
### How to compile:
-1. If you have CUDA 8.0, OpenCV 2.4.9 (C:\opencv_2.4.9) and MSVS 2015 then start MSVS, open `build\darknet\darknet.sln` and do the: Build -> Build darknet
+1. If you have MSVS 2015, CUDA 8.0 and OpenCV 2.4.9 (with paths: `C:\opencv_2.4.9\opencv\build\include` & `C:\opencv_2.4.9\opencv\build\x64\vc12\lib` or `vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release**, and do the: Build -> Build darknet
+
+ 1.1. Find files `opencv_core249.dll`, `opencv_highgui249.dll` and `opencv_ffmpeg249_64.dll` in `C:\opencv_2.4.9\opencv\build\x64\vc12\bin` or `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
@@ -83,9 +100,24 @@
3.1 (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories
3.2 (right click on project) -> properties -> Linker -> General -> Additional Library Directories
+
+ 3.3 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")`
+
4. If you have other version of OpenCV 3.x (not 2.4.x) then you should change many places in code by yourself.
+5. If you want to build with CUDNN to speed up then:
+
+ * download and install CUDNN: https://developer.nvidia.com/cudnn
+
+ * add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg
+
+ * open `\darknet.sln` -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add at the beginning of line: `CUDNN;`
+
### How to compile (custom):
Also, you can to create your own `darknet.sln` & `darknet.vcxproj`, this example for CUDA 8.0 and OpenCV 2.4.9
@@ -94,9 +126,9 @@
- (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories, put here:
`C:\opencv_2.4.9\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 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
- add to project all .c & .cu files from `\src`
-- (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here:
+- (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here:
`C:\opencv_2.4.9\opencv\build\x64\vc12\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories)`
- (right click on project) -> properties -> Linker -> Input -> Additional dependecies, put here:
@@ -104,6 +136,12 @@
`..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cudnn.lib;%(AdditionalDependencies)`
- (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions
+- 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:
@@ -111,12 +149,18 @@
`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
+`opencv_core249.dll`, `opencv_highgui249.dll` and `opencv_ffmpeg249_64.dll` in `C:\opencv_2.4.9\opencv\build\x64\vc12\bin` or `vc14\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
+2. Download The Pascal VOC Data and unpack it to directory `build\darknet\x64\data\voc` will be created dir `build\darknet\x64\data\voc\VOCdevkit\`:
+ * http://pjreddie.com/media/files/VOCtrainval_11-May-2012.tar
+ * http://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar
+ * http://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar
+
+ 2.1 Download file `voc_label.py` to dir `build\darknet\x64\data\voc`: http://pjreddie.com/media/files/voc_label.py
3. Download and install Python for Windows: https://www.python.org/ftp/python/3.5.2/python-3.5.2-amd64.exe
@@ -143,7 +187,7 @@
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`)
+ * 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`)
For example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolo-voc.cfg` in such lines:
@@ -169,11 +213,12 @@
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>`
+5. Create `.txt`-file for each `.jpg`-image-file - in the same directory and 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
+ * `<x> <y> <width> <height>` - float values relative to width and height of image, it can be equal from 0.0 to 1.0
+ * for example: `<x> = <absolute_x> / <image_width>` or `<height> = <absolute_height> / <image_height>`
* atention: `<x> <y>` - are center of rectangle (are not top-left corner)
For example for `img1.jpg` you should create `img1.txt` containing:
@@ -196,3 +241,106 @@
8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23`
+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`
+
+ * Also you can get result earlier than all 45000 iterations.
+
+## 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:
+
+1. During training, you will see varying indicators of error, and you should stop when no longer decreases **0.060730 avg**:
+
+ > Region Avg IOU: 0.798363, Class: 0.893232, Obj: 0.700808, No Obj: 0.004567, Avg Recall: 1.000000, count: 8
+ > Region Avg IOU: 0.800677, Class: 0.892181, Obj: 0.701590, No Obj: 0.004574, Avg Recall: 1.000000, count: 8
+ >
+ > **9002**: 0.211667, **0.060730 avg**, 0.001000 rate, 3.868000 seconds, 576128 images
+ > Loaded: 0.000000 seconds
+
+ * **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.
+
+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:
+
+For example, you stopped training after 9000 iterations, but the best result can give one of previous weights (7000, 8000, 9000). It can happen due to overfitting. **Overfitting** - is case when you can detect objects on images from training-dataset, but can't detect ojbects on any others images. You should get weights from **Early Stopping Point**:
+
+
+
+ 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`.
+
+ 2.2 If training is stopped after 9000 iterations, to validate some of previous weights use this commands:
+
+* `darknet.exe detector recall data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights`
+* `darknet.exe detector recall data/obj.data yolo-obj.cfg backup\yolo-obj_8000.weights`
+* `darknet.exe detector recall data/obj.data yolo-obj.cfg backup\yolo-obj_9000.weights`
+
+And comapre last output lines for each weights (7000, 8000, 9000):
+
+> 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)
+
+For example, **bigger IUO** gives weights `yolo-obj_8000.weights` - then **use this weights for detection**.
+
+
+
+
+### Custom object detection:
+
+Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights`
+
+|  |  |
+|---|---|
+
+## How to improve object detection:
+
+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)
+
+2. After training - for detection:
+
+ * Increase network-resolution by set in your `.cfg`-file (`height=608` and `width=608`) or (`height=832` and `width=832`) or (any value multiple of 32) - this increases the precision and makes it possible to detect small objects: [link](https://github.com/AlexeyAB/darknet/blob/47409529d0eb935fa7bafbe2b3484431117269f5/cfg/yolo-voc.cfg#L4)
+
+ * you do not need to train the network again, just use `.weights`-file already trained for 416x416 resolution
+ * if error `Out of memory` occurs then in `.cfg`-file you should increase `subdivisions=16`, 32 or 64: [link](https://github.com/AlexeyAB/darknet/blob/47409529d0eb935fa7bafbe2b3484431117269f5/cfg/yolo-voc.cfg#L3)
+
+## How to mark bounded boxes of objects and create annotation files:
+
+Here you can find repository with GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2: https://github.com/AlexeyAB/Yolo_mark
+
+With example of: `train.txt`, `obj.names`, `obj.data`, `yolo-obj.cfg`, `air`1-6`.txt`, `bird`1-4`.txt` for 2 classes of objects (air, bird) and `train_obj.cmd` with example how to train this image-set with Yolo v2
+
+## 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**
+ * 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
+
+ * you can run your console application from Windows Explorer `build\darknet\x64\yolo_console_dll.exe`
+ * or you can run from MSVS2015 (before this - you should copy 2 files `yolo-voc.cfg` and `yolo-voc.weights` to the directory `build\darknet\` )
+ * after launching your console application and entering the image file name - you will see info for each object:
+ `<obj_id> <left_x> <top_y> <width> <height> <probability>`
+ * to use simple OpenCV-GUI you should uncomment line `//#define OPENCV` in `yolo_console_dll.cpp`-file: [link](https://github.com/AlexeyAB/darknet/blob/a6cbaeecde40f91ddc3ea09aa26a03ab5bbf8ba8/src/yolo_console_dll.cpp#L5)
+
+`yolo_cpp_dll.dll`-API: [link](https://github.com/AlexeyAB/darknet/blob/master/src/yolo_v2_class.hpp#L31)
+```
+class Detector {
+public:
+ Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0);
+ ~Detector();
+
+ std::vector<bbox_t> detect(std::string image_filename, float thresh = 0.2);
+ std::vector<bbox_t> detect(image_t img, float thresh = 0.2);
+
+#ifdef OPENCV
+ std::vector<bbox_t> detect(cv::Mat mat, float thresh = 0.2);
+#endif
+};
+```
--
Gitblit v1.10.0