Yolo-v3 and Yolo-v2 for Windows and Linux 翻译

Yolo-v3 and Yolo-v2 for Windows and Linux（适用于Windows和Linux的Yolo-v3和Yolo-v2）

(neural network for object detection) - Tensor Cores can be used on Linux and Windows（用于目标检测的神经网络）-张量核可以在Linux和Windows上使用

More details 更多细节: http://pjreddie.com/darknet/yolo/

• Requirements (and how to install dependecies) 需求（以及如何安装依赖项）
• Pre-trained models 预训练模型
• Explanations in issues 问题说明
• Yolo v3 in other frameworks (TensorRT, TensorFlow, PyTorch, OpenVINO, OpenCV-dnn,…) 其他框架（TensorRT，TensorFlow，PyTorch，OpenVINO，OpenCV-dnn等）中的Yolo v3
• Datasets 数据集

0. Improvements in this repository 此存储库中的改进
1. How to use 如何使用
2. How to compile on Linux 如何在Linux上编译
   • Using cmake 使用cmake
   • Using make 使用make
3. How to compile on Windows 如何在Windows上编译
   • Using CMake-GUI 使用CMake-GUI
   • Using vcpkg 使用vcpkg
   • Legacy way 传统方式
4. How to train (Pascal VOC Data) 如何训练（Pascal VOC数据）
5. How to train with multi-GPU 如何使用多GPU进行训练
6. How to train (to detect your custom objects) 如何训练（检测您的自定义对象）
7. How to train tiny-yolo (to detect your custom objects) 如何训练tiny-yolo（检测您的自定义对象）
8. When should I stop training 我什么时候应该停止训练
9. How to calculate mAP on PascalVOC 2007 如何在Pascal VOC 2007上计算mAP
10. How to improve object detection 如何改善物体检测
11. How to mark bounded boxes of objects and create annotation files 如何标记对象的有界框并创建标注文件
12. How to use Yolo as DLL and SO libraries 如何将Yolo用作DLL和SO库

	[email protected] (AP50) https://pjreddie.com/media/files/papers/YOLOv3.pdf

• YOLOv3-spp better than YOLOv3 - mAP = 60.6%, FPS = 20: https://pjreddie.com/darknet/yolo/ YOLOv3-spp优于YOLOv3-mAP = 60.6％，FPS = 20

• Yolo v3 source chart for the RetinaNet on MS COCO got from Table 1 (e): https://arxiv.org/pdf/1708.02002.pdf 表1（e）获得了MS COCO上RetinaNet的Yolo v3源图表

• Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg Pascal VOC 2007上的Yolo v2

• Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg Pascal VOC 2012上的Yolo v2（comp4）

Requirements （需求）

• Windows or Linux
• CMake >= 3.8 for modern CUDA support: https://cmake.org/download/
• CUDA 10.0: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do Post-installation Actions)
• OpenCV >= 2.4: use your preferred package manager (brew, apt), build from source using vcpkg or download from OpenCV official site (on Windows set system variable OpenCV_DIR = C:\opencv\build - where are the include and x64 folders image)
• cuDNN >= 7.0 for CUDA 10.0 https://developer.nvidia.com/rdp/cudnn-archive (on Linux copy cudnn.h,libcudnn.so… as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on Windows copy cudnn.h,cudnn64_7.dll, cudnn64_7.lib as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows )
• GPU with CC >= 3.0: https://en.wikipedia.org/wiki/CUDA#GPUs_supported
• on Linux GCC or Clang, on Windows MSVC 2015/2017/2019 https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community

Pre-trained models

There are weights-file for different cfg-files (smaller size -> faster speed & lower accuracy:

• yolov3-openimages.cfg (247 MB COCO Yolo v3) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-openimages.weights
• yolov3-spp.cfg (240 MB COCO Yolo v3) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-spp.weights
• yolov3.cfg (236 MB COCO Yolo v3) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3.weights
• yolov3-tiny.cfg (34 MB COCO Yolo v3 tiny) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-tiny.weights
• enet-coco.cfg (EfficientNetb0-Yolo- 45.5% [email protected] - 3.7 BFlops) enetb0-coco_final.weights and yolov3-tiny-prn.cfg (33.1% [email protected] - 3.5 BFlops - more)

CLICK ME - Yolo v2 models

• yolov2.cfg (194 MB COCO Yolo v2) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov2.weights
• yolo-voc.cfg (194 MB VOC Yolo v2) - requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo-voc.weights
• yolov2-tiny.cfg (43 MB COCO Yolo v2) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov2-tiny.weights
• yolov2-tiny-voc.cfg (60 MB VOC Yolo v2) - requires 1 GB GPU-RAM: http://pjreddie.com/media/files/yolov2-tiny-voc.weights
• yolo9000.cfg (186 MB Yolo9000-model) - requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo9000.weights

Put it near compiled: darknet.exe

You can get cfg-files by path: darknet/cfg/

Yolo v3 in other frameworks 其他框架下的Yolo v3

• TensorFlow: convert yolov3.weights/cfg files to yolov3.ckpt/pb/meta: by using mystic123 or jinyu121 projects, and TensorFlow-lite
• Intel OpenVINO 2019 R1: (Myriad X / USB Neural Compute Stick / Arria FPGA): read this manual
• OpenCV-dnn is a very fast DNN implementation on CPU (x86/ARM-Android), use yolov3.weights/cfg with: C++ example, Python example
• PyTorch > ONNX > CoreML > iOS how to convert cfg/weights-files to pt-file: ultralytics/yolov3 and iOS App
• TensorRT for YOLOv3 (-70% faster inference 推理速度提高-70％): Yolo is natively supported in DeepStream 4.0
• TVM - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about

Datasets

• MS COCO: use ./scripts/get_coco_dataset.sh to get labeled MS COCO detection dataset
• OpenImages: use python ./scripts/get_openimages_dataset.py for labeling train detection dataset
• Pascal VOC: use python ./scripts/voc_label.py for labeling Train/Test/Val detection datasets
• ILSVRC2012 (ImageNet classification): use ./scripts/get_imagenet_train.sh (also imagenet_label.sh for labeling valid set)
• German/Belgium/Russian/LISA/MASTIF Traffic Sign Datasets for Detection - use this parsers: https://github.com/angeligareta/Datasets2Darknet#detection-task
• List of other datasets: https://github.com/AlexeyAB/darknet/tree/master/scripts#datasets

Examples of results

Others: https://www.youtube.com/user/pjreddie/videos

Improvements in this repository

• added support for Windows
• improved binary neural network performance 2x-4x times for Detection on CPU and GPU if you trained your own weights by using this XNOR-net model (bit-1 inference) : https://github.com/AlexeyAB/darknet/blob/master/cfg/yolov3-tiny_xnor.cfg
• improved neural network performance ~7% by fusing 2 layers into 1: Convolutional + Batch-norm
• improved neural network performance Detection 3x times, Training 2 x times on GPU Volta (Tesla V100, Titan V, …) using Tensor Cores if CUDNN_HALF defined in the Makefile or darknet.sln
• improved performance ~1.2x times on FullHD, ~2x times on 4K, for detection on the video (file/stream) using darknet detector demo…
• improved performance 3.5 X times of data augmentation for training (using OpenCV SSE/AVX functions instead of hand-written functions) - removes bottleneck for training on multi-GPU or GPU Volta
• improved performance of detection and training on Intel CPU with AVX (Yolo v3 ~85%, Yolo v2 ~10%)
• fixed usage of [reorg]-layer
• optimized memory allocation during network resizing when random=1
• optimized initialization GPU for detection - we use batch=1 initially instead of re-init with batch=1
• added correct calculation of mAP, F1, IoU, Precision-Recall using command darknet detector map…
• added drawing of chart of average-Loss and accuracy-mAP (-map flag) during training
• run ./darknet detector demo ... -json_port 8070 -mjpeg_port 8090 as JSON and MJPEG server to get results online over the network by using your soft or Web-browser
• added calculation of anchors for training
• added example of Detection and Tracking objects: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp
• fixed code for use Web-cam on OpenCV > 3.x
• run-time tips and warnings if you use incorrect cfg-file or dataset
• many other fixes of code…

And added manual - How to train Yolo v3/v2 (to detect your custom objects)

Also, you might be interested in using a simplified repository where is implemented INT8-quantization (+30% speedup and -1% mAP reduced): https://github.com/AlexeyAB/yolo2_light

How to use on the command line

On Linux use ./darknet instead of darknet.exe, like this:./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights

On Linux find executable file ./darknet in the root directory, while on Windows find it in the directory \build\darknet\x64

• Yolo v3 COCO - image: darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25
• Output coordinates of objects: darknet.exe detector test cfg/coco.data yolov3.cfg yolov3.weights -ext_output dog.jpg
• Yolo v3 COCO - video: darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights -ext_output test.mp4
• Yolo v3 COCO - WebCam 0: darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights -c 0
• Yolo v3 COCO for net-videocam - Smart WebCam: darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg
• Yolo v3 - save result videofile res.avi: darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights test.mp4 -out_filename res.avi
• Yolo v3 Tiny COCO - video: darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4
• JSON and MJPEG server that allows multiple connections from your soft or Web-browser ip-address:8070 and 8090: ./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output
• Yolo v3 Tiny on GPU #1: darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 1 test.mp4
• Alternative method Yolo v3 COCO - image: darknet.exe detect cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25
• Train on Amazon EC2, to see mAP & Loss-chart using URL like: http://ec2-35-160-228-91.us-west-2.compute.amazonaws.com:8090 in the Chrome/Firefox (Darknet should be compiled with OpenCV):
  ./darknet detector train cfg/coco.data yolov3.cfg darknet53.conv.74 -dont_show -mjpeg_port 8090 -map
• 186 MB Yolo9000 - image: darknet.exe detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights
• Remeber to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app
• To process a list of images data/train.txt and save results of detection to result.json file use:
  darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -ext_output -dont_show -out result.json < data/train.txt
• To process a list of images data/train.txt and save results of detection to result.txt use:
  darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -dont_show -ext_output < data/train.txt > result.txt
• Pseudo-lableing - to process a list of images data/new_train.txt and save results of detection in Yolo training format for each image as label .txt (in this way you can increase the amount of training data) use:
  darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt
• To calculate anchors: darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416
• To check accuracy mAP@IoU=50: darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights
• To check accuracy mAP@IoU=75: darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights -iou_thresh 0.75

For using network video-camera mjpeg-stream with any Android smartphone

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.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

4. Replace the address below, on shown in the phone application (Smart WebCam) and launch:

• Yolo v3 COCO-model: darknet.exe detector demo data/coco.data yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0

How to compile on Linux (using cmake)

The CMakeLists.txt will attempt to find installed optional dependencies like
CUDA, cudnn, ZED and build against those. It will also create a shared object
library file to use darknet for code development.

Do inside the cloned repository:

mkdir build-release
cd build-release
cmake ..
make
make install

How to compile on Linux (using make)

Just do make in the darknet directory.
Before make, you can set such options in the Makefile: link

• GPU=1 to build with CUDA to accelerate by using GPU (CUDA should be in /usr/local/cuda)
• CUDNN=1 to build with cuDNN v5-v7 to accelerate training by using GPU (cuDNN should be in /usr/local/cudnn)
• CUDNN_HALF=1 to build for Tensor Cores (on Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x
• OPENCV=1 to build with OpenCV 4.x/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 support to accelerate Yolo by using multi-core CPU
• LIBSO=1 to build a library darknet.so and binary runable file uselib that uses this library. Or you can try to run so LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib test.mp4 How to use this SO-library from your own code - you can look at C++ example: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp
  or use in such a way: LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov3.cfg yolov3.weights test.mp4
• ZED_CAMERA=1 to build a library with ZED-3D-camera support (should be ZED SDK installed), then run
  LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov3.cfg yolov3.weights zed_camera

To run Darknet on Linux use examples from this article, just use ./darknet instead of darknet.exe, i.e. use this command: ./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights

How to compile on Windows (using CMake-GUI)

This is the recommended approach to build Darknet on Windows if you have already
installed Visual Studio 2015/2017/2019, CUDA > 10.0, cuDNN > 7.0, and
OpenCV > 2.4.

Use CMake-GUI as shown here on this IMAGE:

1. Configure
2. Optional platform for generator (Set: x64)
3. Finish
4. Generate
5. Open Project
6. Set: x64 & Release
7. Build
8. Build solution

How to compile on Windows (using vcpkg)

If you have already installed Visual Studio 2015/2017/2019, CUDA > 10.0,
cuDNN > 7.0, OpenCV > 2.4, then to compile Darknet it is recommended to use
CMake-GUI.

Otherwise, follow these steps:

1. Install or update Visual Studio to at least version 2017, making sure to have it fully patched (run again the installer if not sure to automatically update to latest version). If you need to install from scratch, download VS from here: Visual Studio Community

2. Install CUDA and cuDNN

3. Install git and cmake. Make sure they are on the Path at least for the current account

4. Install vcpkg and try to install a test library to make sure everything is working, for example vcpkg install opengl

5. Define an environment variables, VCPKG_ROOT, pointing to the install path of vcpkg

6. Define another environment variable, with name VCPKG_DEFAULT_TRIPLET and value x64-windows

7. Open Powershell and type these commands:

PS \>                  cd $env:VCPKG_ROOT
PS Code\vcpkg>         .\vcpkg install pthreads opencv[ffmpeg] #replace with opencv[cuda,ffmpeg] in case you want to use cuda-accelerated openCV

8. Open Powershell, go to the darknet folder and build with the command .\build.ps1. If you want to use Visual Studio, you will find two custom solutions created for you by CMake after the build, one in build_win_debug and the other in build_win_release, containing all the appropriate config flags for your system.

How to compile on Windows (legacy way)

1. If you have CUDA 10.0, cuDNN 7.4 and OpenCV 3.x (with paths: C:\opencv_3.0\opencv\build\include & C:\opencv_3.0\opencv\build\x64\vc14\lib), then open build\darknet\darknet.sln, set x64 and Release https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. Also add Windows system variable CUDNN with path to CUDNN: https://user-images.githubusercontent.com/4096485/53249764-019ef880-36ca-11e9-8ffe-d9cf47e7e462.jpg

   1.1. Find files opencv_world320.dll and opencv_ffmpeg320_64.dll (or opencv_world340.dll and opencv_ffmpeg340_64.dll) in C:\opencv_3.0\opencv\build\x64\vc14\bin and put it near with darknet.exe

   1.2 Check that there are bin and include folders in the C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0 if aren’t, then copy them to this folder from the path where is CUDA installed

   1.3. To install CUDNN (speedup neural network), do the following:

   • download and install cuDNN v7.4.1 for CUDA 10.0: https://developer.nvidia.com/rdp/cudnn-archive

   • add Windows system variable CUDNN with path to CUDNN: https://user-images.githubusercontent.com/4096485/53249764-019ef880-36ca-11e9-8ffe-d9cf47e7e462.jpg

   • copy file cudnn64_7.dll to the folder \build\darknet\x64 near with darknet.exe

   1.4. If you want to build without CUDNN then: open \darknet.sln -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and remove this: CUDNN;

2. If you have other version of CUDA (not 10.0) then open build\darknet\darknet.vcxproj by using Notepad, find 2 places with “CUDA 10.0” and change it to your CUDA-version. Then open \darknet.sln -> (right click on project) -> properties -> CUDA C/C++ -> Device and remove there ;compute_75,sm_75. Then do step 1

3. If you don’t have GPU, but have OpenCV 3.0 (with paths: C:\opencv_3.0\opencv\build\include & C:\opencv_3.0\opencv\build\x64\vc14\lib), then open build\darknet\darknet_no_gpu.sln, set x64 and Release, and do the: Build -> Build darknet_no_gpu

4. If you have OpenCV 2.4.13 instead of 3.0 then you should change paths after \darknet.sln is opened

   4.1 (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories: C:\opencv_2.4.13\opencv\build\include

   4.2 (right click on project) -> properties -> Linker -> General -> Additional Library Directories: C:\opencv_2.4.13\opencv\build\x64\vc14\lib

5. If you have GPU with Tensor Cores (nVidia Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x:
   \darknet.sln -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add here: CUDNN_HALF;

   Note: CUDA must be installed only after Visual Studio has been installed.

How to compile (custom):

Also, you can to create your own darknet.sln & darknet.vcxproj, this example for CUDA 9.1 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_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(CUDNN)\include

• (right click on project) -> Build dependecies -> Build Customizations -> set check on CUDA 9.1 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 files
  • all .cu files
  • file http_stream.cpp from \src directory
  • file darknet.h from \include directory
• (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here:

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

OPENCV;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)

• compile to .exe (X64 & Release) and put .dll-s near with .exe: https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg

  • pthreadVC2.dll, pthreadGC2.dll from \3rdparty\dll\x64

  • cusolver64_91.dll, curand64_91.dll, cudart64_91.dll, cublas64_91.dll - 91 for CUDA 9.1 or your version, from C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1\bin

  • For OpenCV 3.2: opencv_world320.dll and opencv_ffmpeg320_64.dll from C:\opencv_3.0\opencv\build\x64\vc14\bin

  • For OpenCV 2.4.13: opencv_core2413.dll, opencv_highgui2413.dll and opencv_ffmpeg2413_64.dll from C:\opencv_2.4.13\opencv\build\x64\vc14\bin

How to train (Pascal VOC Data):

1. Download pre-trained weights for the convolutional layers (154 MB): http://pjreddie.com/media/files/darknet53.conv.74 and put to the directory build\darknet\x64

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

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. Set batch=64 and subdivisions=8 in the file yolov3-voc.cfg: link

7. Start training by using train_voc.cmd or by using the command line:

   darknet.exe detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74

(Note: To disable Loss-Window use flag -dont_show. If you are using CPU, try darknet_no_gpu.exe instead of darknet.exe.)

If required change paths in the file build\darknet\cfg\voc.data

More information about training by the link: http://pjreddie.com/darknet/yolo/#train-voc

Note: If during training you see nan values for avg (loss) field - then training goes wrong, but if nan is in some other lines - then training goes well.

How to train with multi-GPU:

1. Train it first on 1 GPU for like 1000 iterations: darknet.exe detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74

2. Then stop and by using partially-trained model /backup/yolov3-voc_1000.weights run training with multigpu (up to 4 GPUs): darknet.exe detector train cfg/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3

Only for small datasets sometimes better to decrease learning rate, for 4 GPUs set learning_rate = 0.00025 (i.e. learning_rate = 0.001 / GPUs). In this case also increase 4x times burn_in = and max_batches = in your cfg-file. I.e. use burn_in = 4000 instead of 1000. Same goes for steps= if policy=steps is set.

https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ

How to train (to detect your custom objects):

(to train old Yolo v2 yolov2-voc.cfg, yolov2-tiny-voc.cfg, yolo-voc.cfg, yolo-voc.2.0.cfg, … click by the link) 训练旧的Yolo v2 yolov2-voc.cfg，yolov2-tiny-voc.cfg，yolo-voc.cfg，yolo-voc.2.0.cfg

Training Yolo v3 训练Yolo v3:

1. Create file yolo-obj.cfg with the same content as in yolov3.cfg (or copy yolov3.cfg to yolo-obj.cfg) and 创建文件yolo-obj.cfg，其内容与yolov3.cfg中的内容相同（或将yolov3.cfg复制到yolo-obj.cfg），然后:

• change line batch to batch=64 将批次行更改为 batch= 64
• change line subdivisions to subdivisions=8 将细分行更改为 subdivisions= 8
• change line max_batches to (classes*2000 but not less than 4000), f.e. max_batches=6000 if you train for 3 classes 将行最大批次行更改为（classs * 2000但不少于4000），例如您训练3个类，则 max_batches=6000
• change line steps to 80% and 90% of max_batches, f.e. steps=4800,5400 将步长更改为最大批次的80％和90％，例如 steps= 4800,5400
• change line classes=80 to your number of objects in each of 3 [yolo]-layers 将3层[yolo]中每一层的“ classes = 80”行更改为你的对象数:
  • https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L610
  • https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L696
  • https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L783
• change [filters=255] to filters=(classes + 5)x3 in the 3 [convolutional] before each [yolo] layer
  • https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L603
  • https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L689
  • https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L776

So if classes=1 then should be filters=18. If classes=2 then write filters=21.

(Do not write in the cfg-file: filters=(classes + 5)x3)

(Generally filters depends on the classes, coords and number of masks, i.e. filters=(classes + coords + 1)*, where mask is indices of anchors. If mask is absence, then filters=(classes + coords + 1)*num)

So for example, for 2 objects, your file yolo-obj.cfg should differ from yolov3.cfg in such lines in each of 3 [yolo]-layers:

[convolutional]
filters=21

[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  = data/train.txt
valid  = data/test.txt
names = data/obj.names
backup = backup/

4. Put image-files (.jpg) of your objects in the directory build\darknet\x64\data\obj\

5. You should label each object on images from your dataset. Use this visual GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2 & v3: https://github.com/AlexeyAB/Yolo_mark

It will 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:

Where:

• - integer object number from 0 to (classes-1)
• - float values relative to width and height of image, it can be equal from (0.0 to 1.0]
• for example: = / or = /
• atention: - are center of rectangle (are not top-left corner)

For example for img1.jpg you will be created 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 (154 MB): https://pjreddie.com/media/files/darknet53.conv.74 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 darknet53.conv.74

   To train on Linux use command: ./darknet detector train data/obj.data yolo-obj.cfg darknet53.conv.74 (just use ./darknet instead of darknet.exe)

   • (file yolo-obj_last.weights will be saved to the build\darknet\x64\backup\ for each 100 iterations)
   • (file yolo-obj_xxxx.weights will be saved to the build\darknet\x64\backup\ for each 1000 iterations)
   • (to disable Loss-Window use darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -dont_show, if you train on computer without monitor like a cloud Amazon EC2)
   • (to see the mAP & Loss-chart during training on remote server without GUI, use command darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -dont_show -mjpeg_port 8090 -map then open URL http://ip-address:8090 in Chrome/Firefox browser)

8.1. For training with mAP (mean average precisions) calculation for each 4 Epochs (set valid=valid.txt or train.txt in obj.data file) and run: darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -map

9. After training is complete - get result yolo-obj_final.weights from path build\darknet\x64\backup\

• After each 100 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just start training using: darknet.exe detector train data/obj.data yolo-obj.cfg backup\yolo-obj_2000.weights

  (in the original repository https://github.com/pjreddie/darknet the weights-file is saved only once every 10 000 iterations if(iterations > 1000))

• Also you can get result earlier than all 45000 iterations.

Note: If during training you see nan values for avg (loss) field - then training goes wrong, but if nan is in some other lines - then training goes well.

Note: If you changed width= or height= in your cfg-file, then new width and height must be divisible by 32.

Note: After training use such command for detection: darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights

Note: if error Out of memory occurs then in .cfg-file you should increase subdivisions=16, 32 or 64: link

How to train tiny-yolo (to detect your custom objects):

Do all the same steps as for the full yolo model as described above. With the exception of:

• Download default weights file for yolov3-tiny: https://pjreddie.com/media/files/yolov3-tiny.weights
• Get pre-trained weights yolov3-tiny.conv.15 using command: darknet.exe partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15
• Make your custom model yolov3-tiny-obj.cfg based on cfg/yolov3-tiny_obj.cfg instead of yolov3.cfg
• Start training: darknet.exe detector train data/obj.data yolov3-tiny-obj.cfg yolov3-tiny.conv.15

For training Yolo based on other models (DenseNet201-Yolo or ResNet50-Yolo), you can download and get pre-trained weights as showed in this file: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/partial.cmd
If you made you custom model that isn’t based on other models, then you can train it without pre-trained weights, then will be used random initial weights.

When should I stop training:

Usually sufficient 2000 iterations for each class(object), but not less than 4000 iterations in total. 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.XXXXXXX 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, 0.60730 avg, 0.001000 rate, 3.868000 seconds, 576128 images
Loaded: 0.000000 seconds

• 9002 - iteration number (number of batch)
• 0.60730 avg - average loss (error) - the lower, the better

When you see that average loss 0.xxxxxx avg no longer decreases at many iterations then you should stop training. The final avgerage loss can be from 0.05 (for a small model and easy dataset) to 3.0 (for a big model and a difficult dataset).

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 objects on any others images. You should get weights from Early Stopping Point:

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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:

(If you use another GitHub repository, then use darknet.exe detector recall… instead of darknet.exe detector map…)

• darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights
• darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_8000.weights
• darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_9000.weights

And comapre last output lines for each weights (7000, 8000, 9000):

Choose weights-file with the highest mAP (mean average precision) or IoU (intersect over union)

For example, bigger mAP gives weights yolo-obj_8000.weights - then use this weights for detection.

Or just train with -map flag:

darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -map

So you will see mAP-chart (red-line) in the Loss-chart Window. mAP will be calculated for each 4 Epochs using valid=valid.txt file that is specified in obj.data file (1 Epoch = images_in_train_txt / batch iterations)

(to change the max x-axis value - change max_batches= parameter to 2000*classes, f.e. max_batches=6000 for 3 classes)

Example of custom object detection: darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights

• IoU (intersect over union) - average instersect over union of objects and detections for a certain threshold = 0.24

• mAP (mean average precision) - mean value of average precisions for each class, where average precision is average value of 11 points on PR-curve for each possible threshold (each probability of detection) for the same class (Precision-Recall in terms of PascalVOC, where Precision=TP/(TP+FP) and Recall=TP/(TP+FN) ), page-11: http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc09.pdf

mAP is default metric of precision in the PascalVOC competition, this is the same as AP50 metric in the MS COCO competition.
In terms of Wiki, indicators Precision and Recall have a slightly different meaning than in the PascalVOC competition, but IoU always has the same meaning.

How to calculate mAP on PascalVOC 2007:

1. To calculate mAP (mean average precision) on PascalVOC-2007-test:

• Download PascalVOC dataset, install Python 3.x and get file 2007_test.txt as described here: https://github.com/AlexeyAB/darknet#how-to-train-pascal-voc-data
• Then download file https://raw.githubusercontent.com/AlexeyAB/darknet/master/scripts/voc_label_difficult.py to the dir build\darknet\x64\data\ then run voc_label_difficult.py to get the file difficult_2007_test.txt
• Remove symbol # from this line to un-comment it: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/data/voc.data#L4
• Then there are 2 ways to get mAP:
  1. Using Darknet + Python: run the file build/darknet/x64/calc_mAP_voc_py.cmd - you will get mAP for yolo-voc.cfg model, mAP = 75.9%
  2. Using this fork of Darknet: run the file build/darknet/x64/calc_mAP.cmd - you will get mAP for yolo-voc.cfg model, mAP = 75.8%

(The article specifies the value of mAP = 76.8% for YOLOv2 416×416, page-4 table-3: https://arxiv.org/pdf/1612.08242v1.pdf. We get values lower - perhaps due to the fact that the model was trained on a slightly different source code than the code on which the detection is was done)

• if you want to get mAP for tiny-yolo-voc.cfg model, then un-comment line for tiny-yolo-voc.cfg and comment line for yolo-voc.cfg in the .cmd-file
• if you have Python 2.x instead of Python 3.x, and if you use Darknet+Python-way to get mAP, then in your cmd-file use reval_voc.py and voc_eval.py instead of reval_voc_py3.py and voc_eval_py3.py from this directory: https://github.com/AlexeyAB/darknet/tree/master/scripts

Custom object detection:

Example of custom object detection: darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights

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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

• increase network resolution in your .cfg-file (height=608, width=608 or any value multiple of 32) - it will increase precision

• check that each object that you want to detect is mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, …). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark

• my Loss is very high and mAP is very low, is training wrong? Run training with -show_imgs flag at the end of training command, do you see correct bounded boxes of objects (in windows or in files aug_...jpg)? If no - your training dataset is wrong.

• for each object which you want to detect - there must be at least 1 similar object in the Training dataset with about the same: shape, side of object, relative size, angle of rotation, tilt, illumination. So desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 different images for each class or more, and you should train 2000*classes iterations or more

• desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty .txt files) - use as many images of negative samples as there are images with objects

• What is the best way to mark objects: label only the visible part of the object, or label the visible and overlapped part of the object, or label a little more than the entire object (with a little gap)? Mark as you like - how would you like it to be detected.

• for training with a large number of objects in each image, add the parameter max=200 or higher value in the last [yolo]-layer or [region]-layer in your cfg-file (the global maximum number of objects that can be detected by YoloV3 is 0,0615234375*(width*height) where are width and height are parameters from [net] section in cfg-file)

• for training for small objects (smaller than 16x16 after the image is resized to 416x416) - set layers = -1, 11 instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L720
  and set stride=4 instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L717

• for training for both small and large objects use modified models:

  • Full-model: 5 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3_5l.cfg
  • Tiny-model: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny_3l.cfg
  • Spatial-full-model: 3 yolo layers: https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-spp.cfg

• If you train the model to distinguish Left and Right objects as separate classes (left/right hand, left/right-turn on road signs, …) then for disabling flip data augmentation - add flip=0 here: https://github.com/AlexeyAB/darknet/blob/3d2d0a7c98dbc8923d9ff705b81ff4f7940ea6ff/cfg/yolov3.cfg#L17

• General rule - your training dataset should include such a set of relative sizes of objects that you want to detect:

  • train_network_width * train_obj_width / train_image_width ~= detection_network_width * detection_obj_width / detection_image_width
  • train_network_height * train_obj_height / train_image_height ~= detection_network_height * detection_obj_height / detection_image_height

  I.e. for each object from Test dataset there must be at least 1 object in the Training dataset with the same class_id and about the same relative size:

  object width in percent from Training dataset ~= object width in percent from Test dataset

  That is, if only objects that occupied 80-90% of the image were present in the training set, then the trained network will not be able to detect objects that occupy 1-10% of the image.

• to speedup training (with decreasing detection accuracy) do Fine-Tuning instead of Transfer-Learning, set param stopbackward=1 here: https://github.com/AlexeyAB/darknet/blob/6d44529cf93211c319813c90e0c1adb34426abe5/cfg/yolov3.cfg#L548
  then do this command: ./darknet partial cfg/yolov3.cfg yolov3.weights yolov3.conv.81 81 will be created file yolov3.conv.81,
  then train by using weights file yolov3.conv.81 instead of darknet53.conv.74

• each: model of object, side, illimination, scale, each 30 grad of the turn and inclination angles - these are different objects from an internal perspective of the neural network. So the more different objects you want to detect, the more complex network model should be used.

• Only if you are an expert in neural detection networks - recalculate anchors for your dataset for width and height from cfg-file:
  darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416
  then set the same 9 anchors in each of 3 [yolo]-layers in your cfg-file. But you should change indexes of anchors masks= for each [yolo]-layer, so that 1st-[yolo]-layer has anchors larger than 60x60, 2nd larger than 30x30, 3rd remaining. Also you should change the filters=(classes + 5)* before each [yolo]-layer. If many of the calculated anchors do not fit under the appropriate layers - then just try using all the default anchors.

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

  • it is not necessary to train the network again, just use .weights-file already trained for 416x416 resolution
  • but to get even greater accuracy you should train with higher resolution 608x608 or 832x832, note: if error Out of memory occurs then in .cfg-file you should increase subdivisions=16, 32 or 64: link

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 & v3: https://github.com/AlexeyAB/Yolo_mark

With example of: train.txt, obj.names, obj.data, yolo-obj.cfg, air1-6.txt, bird1-4.txt for 2 classes of objects (air, bird) and train_obj.cmd with example how to train this image-set with Yolo v2 & v3

Different tools for marking objects in images:

1. in C++: https://github.com/AlexeyAB/Yolo_mark
2. in Python: https://github.com/tzutalin/labelImg
3. in Python: https://github.com/Cartucho/OpenLabeling
4. in C++: https://www.ccoderun.ca/darkmark/

Using Yolo9000

Simultaneous detection and classification of 9000 objects: darknet.exe detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights data/dog.jpg

• yolo9000.weights - (186 MB Yolo9000 Model) requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo9000.weights

• yolo9000.cfg - cfg-file of the Yolo9000, also there are paths to the 9k.tree and coco9k.map https://github.com/AlexeyAB/darknet/blob/617cf313ccb1fe005db3f7d88dec04a04bd97cc2/cfg/yolo9000.cfg#L217-L218

  • 9k.tree - WordTree of 9418 categories - , if parent_id == -1 then this label hasn’t parent: https://raw.githubusercontent.com/AlexeyAB/darknet/master/build/darknet/x64/data/9k.tree

  • coco9k.map - map 80 categories from MSCOCO to WordTree 9k.tree: https://raw.githubusercontent.com/AlexeyAB/darknet/master/build/darknet/x64/data/coco9k.map

• combine9k.data - data file, there are paths to: 9k.labels, 9k.names, inet9k.map, (change path to your combine9k.train.list): https://raw.githubusercontent.com/AlexeyAB/darknet/master/build/darknet/x64/data/combine9k.data

  • 9k.labels - 9418 labels of objects: https://raw.githubusercontent.com/AlexeyAB/darknet/master/build/darknet/x64/data/9k.labels

  • 9k.names -
    9418 names of objects: https://raw.githubusercontent.com/AlexeyAB/darknet/master/build/darknet/x64/data/9k.names

  • inet9k.map - map 200 categories from ImageNet to WordTree 9k.tree: https://raw.githubusercontent.com/AlexeyAB/darknet/master/build/darknet/x64/data/inet9k.map

How to use Yolo as DLL and SO libraries

• on Linux
  • using build.sh or
  • build darknet using cmake or
  • set LIBSO=1 in the Makefile and do make
• on Windows
  • using build.ps1 or
  • build darknet using cmake or
  • compile build\darknet\yolo_cpp_dll.sln solution or build\darknet\yolo_cpp_dll_no_gpu.sln solution

There are 2 APIs:

• C API: https://github.com/AlexeyAB/darknet/blob/master/include/darknet.h

  • Python examples using the C API::
    • https://github.com/AlexeyAB/darknet/blob/master/darknet.py
    • https://github.com/AlexeyAB/darknet/blob/master/darknet_video.py

• C++ API: https://github.com/AlexeyAB/darknet/blob/master/include/yolo_v2_class.hpp

  • C++ example that uses C++ API: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp

---

1. To compile Yolo as C++ DLL-file yolo_cpp_dll.dll - open the solution build\darknet\yolo_cpp_dll.sln, set x64 and Release, and do the: Build -> Build yolo_cpp_dll

   • You should have installed CUDA 10.0
   • 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 the solution 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
     use this command: yolo_console_dll.exe data/coco.names yolov3.cfg yolov3.weights test.mp4

   • after launching your console application and entering the image file name - you will see info for each object:
     

   • to use simple OpenCV-GUI you should uncomment line //#define OPENCV in yolo_console_dll.cpp-file: link

   • you can see source code of simple example for detection on the video file: link

yolo_cpp_dll.dll-API: link

struct bbox_t {
    unsigned int x, y, w, h;    // (x,y) - top-left corner, (w, h) - width & height of bounded box
    float prob;                    // confidence - probability that the object was found correctly
    unsigned int obj_id;        // class of object - from range [0, classes-1]
    unsigned int track_id;        // tracking id for video (0 - untracked, 1 - inf - tracked object)
    unsigned int frames_counter;// counter of frames on which the object was detected
};

class Detector {
public:
        Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0);
        ~Detector();

        std::vector detect(std::string image_filename, float thresh = 0.2, bool use_mean = false);
        std::vector 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 detect(cv::Mat mat, float thresh = 0.2, bool use_mean = false);
	std::shared_ptr mat_to_image_resize(cv::Mat mat) const;
#endif
};