mirror of https://github.com/AlexeyAB/darknet.git
453 lines
31 KiB
Markdown
453 lines
31 KiB
Markdown
# Yolo-v2 Windows and Linux version
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[](https://circleci.com/gh/AlexeyAB/darknet)
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1. [How to use](#how-to-use)
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2. [How to compile on Linux](#how-to-compile-on-linux)
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3. [How to compile on Windows](#how-to-compile-on-windows)
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4. [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data)
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5. [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects)
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6. [When should I stop training](#when-should-i-stop-training)
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7. [How to calculate mAP on PascalVOC 2007](#how-to-calculate-map-on-pascalvoc-2007)
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8. [How to improve object detection](#how-to-improve-object-detection)
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9. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files)
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10. [Using Yolo9000](#using-yolo9000)
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11. [How to use Yolo as DLL](#how-to-use-yolo-as-dll)
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|  |  https://arxiv.org/abs/1612.08242 |
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|  |  https://arxiv.org/abs/1612.08242 |
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# "You Only Look Once: Unified, Real-Time Object Detection (version 2)"
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A Yolo cross-platform Windows and Linux version (for object detection). Contributtors: https://github.com/pjreddie/darknet/graphs/contributors
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This repository is forked from Linux-version: https://github.com/pjreddie/darknet
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More details: http://pjreddie.com/darknet/yolo/
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This repository supports:
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* both Windows and Linux
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* both OpenCV 2.x.x and OpenCV <= 3.4.0 (3.4.1 and higher isn't supported)
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* both cuDNN v5-v7
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* CUDA >= 7.5
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* also create SO-library on Linux and DLL-library on Windows
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##### Requires:
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* **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))
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* **CUDA 9.1**: https://developer.nvidia.com/cuda-downloads
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* **OpenCV 3.4.0**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.4.0/opencv-3.4.0-vc14_vc15.exe/download
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* **or OpenCV 2.4.13**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.13/opencv-2.4.13.2-vc14.exe/download
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- OpenCV allows to show image or video detection in the window and store result to file that specified in command line `-out_filename res.avi`
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* **GPU with CC >= 2.0** if you use CUDA, or **GPU CC >= 3.0** if you use cuDNN + CUDA: https://en.wikipedia.org/wiki/CUDA#GPUs_supported
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##### Pre-trained models for different cfg-files can be downloaded from (smaller -> faster & lower quality):
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* `yolo.cfg` (194 MB COCO-model) - require 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo.weights
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* `yolo-voc.cfg` (194 MB VOC-model) - require 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo-voc.weights
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* `tiny-yolo.cfg` (60 MB COCO-model) - require 1 GB GPU-RAM: http://pjreddie.com/media/files/tiny-yolo.weights
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* `tiny-yolo-voc.cfg` (60 MB VOC-model) - require 1 GB GPU-RAM: http://pjreddie.com/media/files/tiny-yolo-voc.weights
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* `yolo9000.cfg` (186 MB Yolo9000-model) - require 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo9000.weights
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Put it near compiled: darknet.exe
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You can get cfg-files by path: `darknet/cfg/`
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##### Examples of results:
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[](https://www.youtube.com/watch?v=VOC3huqHrss "Everything Is AWESOME")
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Others: https://www.youtube.com/channel/UC7ev3hNVkx4DzZ3LO19oebg
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### How to use:
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##### Example of usage in cmd-files from `build\darknet\x64\`:
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* `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
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* `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
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* `darknet_demo_store.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: res.avi
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* `darknet_net_cam_voc.cmd` - initialization with 194 MB VOC-model, play video from network video-camera mjpeg-stream (also from you phone)
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* `darknet_web_cam_voc.cmd` - initialization with 194 MB VOC-model, play video from Web-Camera number #0
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* `darknet_coco_9000.cmd` - initialization with 186 MB Yolo9000 COCO-model, and show detection on the image: dog.jpg
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* `darknet_coco_9000_demo.cmd` - initialization with 186 MB Yolo9000 COCO-model, and show detection on the video (if it is present): street4k.mp4, and store result to: res.avi
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##### How to use on the command line:
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On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector test ./cfg/coco.data ./cfg/yolo.cfg ./yolo.weights`
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* 194 MB COCO-model - image: `darknet.exe detector test data/coco.data yolo.cfg yolo.weights -i 0 -thresh 0.2`
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* Alternative method 194 MB COCO-model - image: `darknet.exe detect yolo.cfg yolo.weights -i 0 -thresh 0.2`
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* 194 MB VOC-model - image: `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -i 0`
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* 194 MB COCO-model - video: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights test.mp4 -i 0`
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* 194 MB VOC-model - video: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights test.mp4 -i 0`
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* 194 MB COCO-model - **save result to the file res.avi**: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights test.mp4 -i 0 -out_filename res.avi`
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* 194 MB VOC-model - **save result to the file res.avi**: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights test.mp4 -i 0 -out_filename res.avi`
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* Alternative method 194 MB VOC-model - video: `darknet.exe yolo demo yolo-voc.cfg yolo-voc.weights test.mp4 -i 0`
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* 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`
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* 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`
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* 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`
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* 194 MB VOC-model - WebCamera #0: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights -c 0`
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* 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data yolo9000.cfg yolo9000.weights`
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* 186 MB Yolo9000 - video: `darknet.exe detector demo cfg/combine9k.data yolo9000.cfg yolo9000.weights test.mp4`
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* 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
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* To process a list of images `data/train.txt` and save results of detection to `result.txt` use:
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`darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -dont_show < data/train.txt > result.txt`
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You can comment this line so that each image does not require pressing the button ESC: https://github.com/AlexeyAB/darknet/blob/6ccb41808caf753feea58ca9df79d6367dedc434/src/detector.c#L509
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##### For using network video-camera mjpeg-stream with any Android smartphone:
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1. Download for Android phone mjpeg-stream soft: IP Webcam / Smart WebCam
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* Smart WebCam - preferably: https://play.google.com/store/apps/details?id=com.acontech.android.SmartWebCam2
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* IP Webcam: https://play.google.com/store/apps/details?id=com.pas.webcam
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2. Connect your Android phone to computer by WiFi (through a WiFi-router) or USB
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3. Start Smart WebCam on your phone
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4. Replace the address below, on shown in the phone application (Smart WebCam) and launch:
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* 194 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`
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* 194 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`
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### How to compile on Linux:
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Just do `make` in the darknet directory.
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Before make, you can set such options in the `Makefile`: [link](https://github.com/AlexeyAB/darknet/blob/9c1b9a2cf6363546c152251be578a21f3c3caec6/Makefile#L1)
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* `GPU=1` to build with CUDA to accelerate by using GPU (CUDA should be in `/usr/local/cuda`)
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* `CUDNN=1` to build with cuDNN v5-v7 to accelerate training by using GPU (cuDNN should be in `/usr/local/cudnn`)
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* `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
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* `DEBUG=1` to bould debug version of Yolo
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* `OPENMP=1` to build with OpenMP support to accelerate Yolo by using multi-core CPU
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* `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
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### How to compile on Windows:
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1. If you have **MSVS 2015, CUDA 9.1 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. **NOTE:** If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see [#500](https://github.com/AlexeyAB/darknet/issues/500)).
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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`
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2. If you have other version of **CUDA (not 9.1)** then open `build\darknet\darknet.vcxproj` by using Notepad, find 2 places with "CUDA 9.1" and change it to your CUDA-version, then do step 1
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3. If you **don't have GPU**, but have **MSVS 2015 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_no_gpu.sln`, set **x64** and **Release**, and do the: Build -> Build darknet_no_gpu
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4. If you have **OpenCV 2.4.13** instead of 3.0 then you should change pathes after `\darknet.sln` is opened
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4.1 (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories: `C:\opencv_2.4.13\opencv\build\include`
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4.2 (right click on project) -> properties -> Linker -> General -> Additional Library Directories: `C:\opencv_2.4.13\opencv\build\x64\vc14\lib`
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5. If you want to build with CUDNN to speed up then:
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* download and install **cuDNN 7.0 for CUDA 9.1**: https://developer.nvidia.com/cudnn
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* Check that there are `bin` and `include` folders in the `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1` if aren't, then copy them to this folder from the path where is CUDA installed
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* add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg
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* open `\darknet.sln` -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add at the beginning of line: `CUDNN;`
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### How to compile (custom):
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Also, you can to create your own `darknet.sln` & `darknet.vcxproj`, this example for CUDA 9.1 and OpenCV 3.0
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Then add to your created project:
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- (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories, put here:
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`C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include`
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- (right click on project) -> Build dependecies -> Build Customizations -> set check on CUDA 9.1 or what version you have - for example as here: http://devblogs.nvidia.com/parallelforall/wp-content/uploads/2015/01/VS2013-R-5.jpg
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- add to project all .c & .cu files from `\src`
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- (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here:
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`C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories)`
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- (right click on project) -> properties -> Linker -> Input -> Additional dependecies, put here:
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`..\..\3rdparty\lib\x64\pthreadVC2.lib;cublas.lib;curand.lib;cudart.lib;cudnn.lib;%(AdditionalDependencies)`
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- (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions
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`OPENCV;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)`
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- compile to .exe (X64 & Release) and put .dll-s near with .exe:
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* `pthreadVC2.dll, pthreadGC2.dll` from \3rdparty\dll\x64
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* `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
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* For OpenCV 3.2: `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` from `C:\opencv_3.0\opencv\build\x64\vc14\bin`
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* 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`
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## How to train (Pascal VOC Data):
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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`
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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\`:
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* http://pjreddie.com/media/files/VOCtrainval_11-May-2012.tar
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* http://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar
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* http://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar
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2.1 Download file `voc_label.py` to dir `build\darknet\x64\data\voc`: http://pjreddie.com/media/files/voc_label.py
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3. Download and install Python for Windows: https://www.python.org/ftp/python/3.5.2/python-3.5.2-amd64.exe
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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)
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5. Run command: `type 2007_train.txt 2007_val.txt 2012_*.txt > train.txt`
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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)
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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` (**Note:** To disable Loss-Window use flag `-dont_show`. If you are using CPU, try `darknet_no_gpu.exe` instead of `darknet.exe`.)
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If required change pathes in the file `build\darknet\x64\data\voc.data`
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More information about training by the link: http://pjreddie.com/darknet/yolo/#train-voc
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## How to train with multi-GPU:
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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`
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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 /backup/yolo-voc_1000.weights -gpus 0,1,2,3`
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https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ
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## How to train (to detect your custom objects):
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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:
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* change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.2.0.cfg#L2)
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* change line subdivisions to [`subdivisions=8`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.2.0.cfg#L3)
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* change line `classes=20` to your number of objects
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* change line #237 from [`filters=125`](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.2.0.cfg#L224) to: filters=(classes + 5)x5, so if `classes=2` then should be `filters=35`. Or if you use `classes=1` then write `filters=30`, **do not write in the cfg-file: filters=(classes + 5)x5**.
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(Generally `filters` depends on the `classes`, `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`, where `num` is number of anchors)
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So for example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolo-voc.2.0.cfg` in such lines:
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```
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[convolutional]
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filters=35
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[region]
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classes=2
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```
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2. Create file `obj.names` in the directory `build\darknet\x64\data\`, with objects names - each in new line
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3. Create file `obj.data` in the directory `build\darknet\x64\data\`, containing (where **classes = number of objects**):
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```
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classes= 2
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train = data/train.txt
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valid = data/test.txt
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names = data/obj.names
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backup = backup/
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```
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4. Put image-files (.jpg) of your objects in the directory `build\darknet\x64\data\obj\`
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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>`
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Where:
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* `<object-class>` - integer number of object from `0` to `(classes-1)`
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* `<x> <y> <width> <height>` - float values relative to width and height of image, it can be equal from 0.0 to 1.0
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* for example: `<x> = <absolute_x> / <image_width>` or `<height> = <absolute_height> / <image_height>`
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* atention: `<x> <y>` - are center of rectangle (are not top-left corner)
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For example for `img1.jpg` you should create `img1.txt` containing:
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```
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1 0.716797 0.395833 0.216406 0.147222
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0 0.687109 0.379167 0.255469 0.158333
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1 0.420312 0.395833 0.140625 0.166667
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```
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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:
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```
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data/obj/img1.jpg
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data/obj/img2.jpg
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data/obj/img3.jpg
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```
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7. 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`
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||
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)
|
||
(To disable Loss-Window use `darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23 -dont_show`, if you train on computer without monitor like a cloud Amazaon EC2)
|
||
|
||
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.
|
||
|
||
### 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 tiny-yolo-voc: http://pjreddie.com/media/files/tiny-yolo-voc.weights
|
||
* Get pre-trained weights tiny-yolo-voc.conv.13 using command: `darknet.exe partial cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights tiny-yolo-voc.conv.13 13`
|
||
* Make your custom model `tiny-yolo-obj.cfg` based on `tiny-yolo-voc.cfg` instead of `yolo-voc.2.0.cfg`
|
||
* Start training: `darknet.exe detector train data/obj.data tiny-yolo-obj.cfg tiny-yolo-voc.conv.13`
|
||
|
||
For training Yolo based on other models ([DenseNet201-Yolo](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/densenet201_yolo.cfg) or [ResNet50-Yolo](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/resnet50_yolo.cfg)), 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 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.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:
|
||
|
||
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**:
|
||
|
||
|
||
|
||
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 IoU** (intersect of union) and mAP (mean average precision)
|
||
|
||
For example, **bigger IOU** gives weights `yolo-obj_8000.weights` - then **use this weights for detection**.
|
||
|
||
Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights`
|
||
|
||
* **IoU** (intersect of union) - average instersect of 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
|
||
|
||
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\voc` 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`
|
||
|
||
|  |  |
|
||
|---|---|
|
||
|
||
## 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/master/cfg/yolo-voc.2.0.cfg#L244)
|
||
|
||
* increase network resolution in your `.cfg`-file (`height=608`, `width=608` or any value multiple of 32) - it will increase precision
|
||
|
||
* desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides
|
||
|
||
* desirable that your training dataset include images with objects (without labels) that you do not want to detect - negative samples
|
||
|
||
* for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last layer [region] in your cfg-file
|
||
|
||
* to speedup training (with decreasing detection accuracy) do Fine-Tuning instead of Transfer-Learning, set param `stopbackward=1` in one of the penultimate convolutional layers, for example here: https://github.com/AlexeyAB/darknet/blob/cad4d1618fee74471d335314cb77070fee951a42/cfg/yolo-voc.2.0.cfg#L202
|
||
|
||
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/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/master/cfg/yolo-voc.2.0.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
|
||
|
||
## Using Yolo9000
|
||
|
||
Simultaneous detection and classification of 9000 objects:
|
||
|
||
* `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 - `<label> <parent_it>`, 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
|
||
|
||
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 9.1**
|
||
* 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)
|
||
* 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#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, 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, bool use_mean = false);
|
||
#endif
|
||
};
|
||
```
|