From 4528f9b4b49dc701d3de7b38fa59c17c41702679 Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Tue, 08 Aug 2017 15:23:57 +0000
Subject: [PATCH] Fixed - use individual track_id for each class of object
---
README.md | 175 +++++++++++++++++++++++++++++++++-------------------------
1 files changed, 100 insertions(+), 75 deletions(-)
diff --git a/README.md b/README.md
index c8af82c..1fe59d6 100644
--- a/README.md
+++ b/README.md
@@ -1,37 +1,43 @@
-# Yolo-Windows v2
+# Yolo-v2 Windows and Linux version
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)
+2. [How to compile on Linux](#how-to-compile-on-linux)
+3. [How to compile on Windows](#how-to-compile-on-windows)
+4. [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data)
+5. [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects)
+6. [When should I stop training](#when-should-i-stop-training)
+7. [How to improve object detection](#how-to-improve-object-detection)
+8. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files)
+9. [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 |
|---|---|
# "You Only Look Once: Unified, Real-Time Object Detection (version 2)"
-A yolo windows version (for object detection)
-
-Contributtors: https://github.com/pjreddie/darknet/graphs/contributors
+A Yolo cross-platform Windows and Linux version (for object detection). Contributtors: https://github.com/pjreddie/darknet/graphs/contributors
This repository is forked from Linux-version: https://github.com/pjreddie/darknet
More details: http://pjreddie.com/darknet/yolo/
+This repository supports:
+
+* both Windows and Linux
+* both OpenCV 3.x and OpenCV 2.4.13
+* both cuDNN 5 and cuDNN 6
+* CUDA >= 7.5
+* also create SO-library on Linux and DLL-library on Windows
+
##### Requires:
-* **MS Visual Studio 2015 (v140)**: https://www.microsoft.com/download/details.aspx?id=48146
-* **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
- - To compile with different OpenCV version - change in file yolo.c each string look like **#pragma comment(lib, "opencv_core249.lib")** from 249 to required version.
- - With OpenCV will show image or video detection in window and store result to: test_dnn_out.avi
+* **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
+* **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: test_dnn_out.avi
##### Pre-trained models for different cfg-files can be downloaded from (smaller -> faster & lower quality):
* `yolo.cfg` (256 MB COCO-model) - require 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo.weights
@@ -53,22 +59,26 @@
##### Example of usage in cmd-files from `build\darknet\x64\`:
-* `darknet_voc.cmd` - initialization with 256 MB VOC-model yolo-voc.weights & yolo-voc.cfg and waiting for entering the name of the image file
-* `darknet_demo_voc.cmd` - initialization with 256 MB VOC-model yolo-voc.weights & yolo-voc.cfg and play your video file which you must rename to: test.mp4, and store result to: test_dnn_out.avi
-* `darknet_net_cam_voc.cmd` - initialization with 256 MB VOC-model, play video from network video-camera mjpeg-stream (also from you phone) and store result to: test_dnn_out.avi
-* `darknet_web_cam_voc.cmd` - initialization with 256 MB VOC-model, play video from Web-Camera number #0 and store result to: test_dnn_out.avi
+* `darknet_voc.cmd` - initialization with 194 MB VOC-model yolo-voc.weights & yolo-voc.cfg and waiting for entering the name of the image file
+* `darknet_demo_voc.cmd` - initialization with 194 MB VOC-model yolo-voc.weights & yolo-voc.cfg and play your video file which you must rename to: test.mp4, and store result to: test_dnn_out.avi
+* `darknet_net_cam_voc.cmd` - initialization with 194 MB VOC-model, play video from network video-camera mjpeg-stream (also from you phone) and store result to: test_dnn_out.avi
+* `darknet_web_cam_voc.cmd` - initialization with 194 MB VOC-model, play video from Web-Camera number #0 and store result to: test_dnn_out.avi
+* `darknet_coco_9000.cmd` - initialization with 186 MB Yolo9000 COCO-model, and show detection on the image: dog.jpg
+* `darknet_coco_9000_demo.cmd` - initialization with 186 MB Yolo9000 COCO-model, and show detection on the video (if it is present): street4k.mp4
##### How to use on the command line:
-* 256 MB COCO-model - image: `darknet.exe detector test data/coco.data yolo.cfg yolo.weights -i 0 -thresh 0.2`
+* 194 MB COCO-model - image: `darknet.exe detector test data/coco.data yolo.cfg yolo.weights -i 0 -thresh 0.2`
* Alternative method 256 MB COCO-model - image: `darknet.exe detect yolo.cfg yolo.weights -i 0 -thresh 0.2`
-* 256 MB VOC-model - image: `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -i 0`
-* 256 MB COCO-model - video: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights test.mp4 -i 0`
-* 256 MB VOC-model - video: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights test.mp4 -i 0`
+* 194 MB VOC-model - image: `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -i 0`
+* 194 MB COCO-model - video: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights test.mp4 -i 0`
+* 194 MB VOC-model - video: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights test.mp4 -i 0`
* Alternative method 256 MB VOC-model - video: `darknet.exe yolo demo yolo-voc.cfg yolo-voc.weights test.mp4 -i 0`
* 60 MB VOC-model for video: `darknet.exe detector demo data/voc.data tiny-yolo-voc.cfg tiny-yolo-voc.weights test.mp4 -i 0`
-* 256 MB COCO-model for net-videocam - Smart WebCam: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0`
-* 256 MB VOC-model for net-videocam - Smart WebCam: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0`
-* 256 MB VOC-model - WebCamera #0: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights -c 0`
+* 194 MB COCO-model for net-videocam - Smart WebCam: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0`
+* 194 MB VOC-model for net-videocam - Smart WebCam: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0`
+* 194 MB VOC-model - WebCamera #0: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights -c 0`
+* 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`
##### For using network video-camera mjpeg-stream with any Android smartphone:
@@ -86,33 +96,37 @@
* 256 MB COCO-model: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0`
* 256 MB VOC-model: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0`
+### How to compile on Linux:
-### How to compile:
+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
+* `CUDNN=1` to build with cuDNN v5/v6 to accelerate training by using GPU
+* `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 suuport to accelerate by using multi-core CPU
+* `LIBSO=1` to build an library `darknet.so` and binary runable file `uselib` that uses this library. How to use this SO-library from your own code - you can look at C++ example: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp
-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`
+### 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
+
+ 1.1. 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`
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
-3. If you have other version of OpenCV 2.4.x (not 2.4.9) then you should change pathes after `\darknet.sln` is opened
+3. If you have OpenCV 2.4.13 instead of 3.0 then you should change pathes after `\darknet.sln` is opened
- 3.1 (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories
+ 3.1 (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories: `C:\opencv_2.4.13\opencv\build\include`
- 3.2 (right click on project) -> properties -> Linker -> General -> Additional Library Directories
+ 3.2 (right click on project) -> properties -> Linker -> General -> Additional Library Directories: `C:\opencv_2.4.13\opencv\build\x64\vc14\lib`
- 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.
+4. If you have other version of OpenCV 2.4.x (not 3.x) then you also should change lines like `#pragma comment(lib, "opencv_core2413.lib")` in the file `\src\detector.c`
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 6.0 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
@@ -120,36 +134,34 @@
### 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
+Also, you can to create your own `darknet.sln` & `darknet.vcxproj`, this example for CUDA 8.0 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_2.4.9\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include`
+`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
- add to project all .c & .cu files from `\src`
- (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)`
+`C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories)`
- (right click on project) -> properties -> Linker -> Input -> Additional dependecies, put here:
`..\..\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), ... :
+`OPENCV;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)`
- * `#pragma comment(lib, "opencv_core249.lib")`
- * `#pragma comment(lib, "opencv_imgproc249.lib")`
- * `#pragma comment(lib, "opencv_highgui249.lib")`
+- open file: `\src\detector.c` and check lines `#pragma` and `#inclue` for OpenCV.
-`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
+ * `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_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`
+ * For OpenCV 3.0: `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` from `C:\opencv_3.0\opencv\build\x64\vc14\bin`
+ * For OpenCV 2.4.13: `opencv_core249.dll`, `opencv_highgui249.dll` and `opencv_ffmpeg249_64.dll` from `C:\opencv_2.4.9\opencv\build\x64\vc14\bin`
## How to train (Pascal VOC Data):
@@ -168,7 +180,9 @@
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.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`
If required change pathes in the file `build\darknet\x64\data\voc.data`
@@ -176,20 +190,22 @@
## 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`
+1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data yolo-voc.2.0.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`
+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.2.0.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:
+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.2.0.cfg#L2)
+ * change line subdivisions to [`subdivisions=8`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.2.0.cfg#L3)
* 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.2.0.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:
+ For example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolo-voc.2.0.cfg` in such lines:
```
[convolutional]
@@ -205,9 +221,9 @@
```
classes= 2
- train = train.txt
- valid = test.txt
- names = obj.names
+ train = data/train.txt
+ valid = data/test.txt
+ names = data/obj.names
backup = backup/
```
@@ -241,6 +257,8 @@
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 until 1000 iterations has been reached, and after for each 1000 iterations)
+
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`
@@ -262,7 +280,7 @@
* **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 +288,9 @@
- 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`.
+To get weights from Early Stopping Point:
+
+ 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:
@@ -283,9 +303,9 @@
> 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**.
+For example, **bigger IOU** gives weights `yolo-obj_8000.weights` - then **use this weights for detection**.
@@ -300,14 +320,16 @@
## 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)
+ * 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)
+
+ * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides
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)
+ * 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/master/cfg/yolo-voc.2.0.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)
+ * 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/master/cfg/yolo-voc.2.0.cfg#L3)
## How to mark bounded boxes of objects and create annotation files:
@@ -328,19 +350,22 @@
* 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)
+ * you can see source code of simple example for detection on the video file: [link](https://github.com/AlexeyAB/darknet/blob/ab1c5f9e57b4175f29a6ef39e7e68987d3e98704/src/yolo_console_dll.cpp#L75)
-`yolo_cpp_dll.dll`-API: [link](https://github.com/AlexeyAB/darknet/blob/master/src/yolo_v2_class.hpp#L31)
+`yolo_cpp_dll.dll`-API: [link](https://github.com/AlexeyAB/darknet/blob/master/src/yolo_v2_class.hpp#L42)
```
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);
+ std::vector<bbox_t> detect(std::string image_filename, float thresh = 0.2, bool use_mean = false);
+ std::vector<bbox_t> detect(image_t img, float thresh = 0.2, bool use_mean = false);
+ static image_t load_image(std::string image_filename);
+ static void free_image(image_t m);
#ifdef OPENCV
- std::vector<bbox_t> detect(cv::Mat mat, float thresh = 0.2);
+ std::vector<bbox_t> detect(cv::Mat mat, float thresh = 0.2, bool use_mean = false);
#endif
};
```
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