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 | 56 +++++++++++++++++++++++++++++++++++++++++++++++++++++---
1 files changed, 53 insertions(+), 3 deletions(-)
diff --git a/README.md b/README.md
index 5c29830..c8af82c 100644
--- a/README.md
+++ b/README.md
@@ -5,9 +5,14 @@
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 mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files)
+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 |
+|---|---|
+
+|  |  https://arxiv.org/abs/1612.08242 |
|---|---|
@@ -144,6 +149,7 @@
`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):
@@ -264,7 +270,9 @@
- If training is stopped after 9000 iterations, to validate some of previous weights use this commands:
+ 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`
@@ -289,8 +297,50 @@
|  |  |
|---|---|
+## 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
+};
+```
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