文件列表:

.circleci (0, 2019-01-26)
.circleci\config.yml (500, 2019-01-26)
3rdparty (0, 2019-01-26)
3rdparty\dll (0, 2019-01-26)
3rdparty\dll\x64 (0, 2019-01-26)
3rdparty\dll\x64\pthreadGC2.dll (185976, 2019-01-26)
3rdparty\dll\x64\pthreadVC2.dll (82944, 2019-01-26)
3rdparty\dll\x86 (0, 2019-01-26)
3rdparty\dll\x86\pthreadGC2.dll (119888, 2019-01-26)
3rdparty\dll\x86\pthreadGCE2.dll (121953, 2019-01-26)
3rdparty\dll\x86\pthreadVC2.dll (55808, 2019-01-26)
3rdparty\dll\x86\pthreadVCE2.dll (61952, 2019-01-26)
3rdparty\dll\x86\pthreadVSE2.dll (57344, 2019-01-26)
3rdparty\include (0, 2019-01-26)
3rdparty\include\pthread.h (42499, 2019-01-26)
3rdparty\include\sched.h (4995, 2019-01-26)
3rdparty\include\semaphore.h (4563, 2019-01-26)
3rdparty\lib (0, 2019-01-26)
3rdparty\lib\x64 (0, 2019-01-26)
3rdparty\lib\x64\libpthreadGC2.a (93692, 2019-01-26)
3rdparty\lib\x64\pthreadVC2.lib (29738, 2019-01-26)
3rdparty\lib\x86 (0, 2019-01-26)
3rdparty\lib\x86\libpthreadGC2.a (93480, 2019-01-26)
3rdparty\lib\x86\libpthreadGCE2.a (93486, 2019-01-26)
3rdparty\lib\x86\pthreadVC2.lib (30334, 2019-01-26)
3rdparty\lib\x86\pthreadVCE2.lib (30460, 2019-01-26)
3rdparty\lib\x86\pthreadVSE2.lib (30460, 2019-01-26)
LICENSE (515, 2019-01-26)
Makefile (4647, 2019-01-26)
build (0, 2019-01-26)
build\darknet (0, 2019-01-26)
build\darknet\YoloWrapper.cs (3039, 2019-01-26)
build\darknet\darknet.sln (1283, 2019-01-26)
build\darknet\darknet.vcxproj (16468, 2019-01-26)
build\darknet\darknet_no_gpu.sln (1281, 2019-01-26)
build\darknet\darknet_no_gpu.vcxproj (16681, 2019-01-26)
build\darknet\x64 (0, 2019-01-26)
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# Yolo-v3 and Yolo-v2 for Windows and Linux ### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows) [](https://circleci.com/gh/AlexeyAB/darknet) * [Requirements](#requirements) * [Pre-trained models](#pre-trained-models) * [Explanations in issues](https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations) 0. [Improvements in this repository](#improvements-in-this-repository) 1. [How to use](#how-to-use) 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 calculate mAP on PascalVOC 2007](#how-to-calculate-map-on-pascalvoc-2007) 8. [How to improve object detection](#how-to-improve-object-detection) 9. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) 10. [Using Yolo9000](#using-yolo9000) 11. [How to use Yolo as DLL](#how-to-use-yolo-as-dll) |  |    mAP (AP50) https://pjreddie.com/media/files/papers/YOLOv3.pdf | |---|---| * YOLOv3-spp (is not indicated) better than YOLOv3 - mAP = 60.6%, FPS = 20: https://pjreddie.com/darknet/yolo/ * Yolo v3 source chart for the RetinaNet on MS COCO got from Table 1 (e): https://arxiv.org/pdf/1708.02002.pdf * Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg * Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg # "You Only Look Once: Unified, Real-Time Object Detection (versions 2 & 3)" A Yolo cross-platform Windows and Linux version (for object detection). Contributtors: https://github.com/AlexeyAB/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 2.x.x and OpenCV <= 3.4.0 (3.4.1 and higher isn't supported, but you can try) * both cuDNN >= v7 * CUDA >= 7.5 * also create SO-library on Linux and DLL-library on Windows ##### Requirements: * **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 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)) * **OpenCV 3.3.0**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.3.0/opencv-3.3.0-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 that specified in command line `-out_filename res.avi` * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported ##### 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 * `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/` ##### Examples of results: [](https://www.youtube.com/watch?v=VOC3huqHrss "Everything Is AWESOME") Others: https://www.youtube.com/channel/UC7ev3hNVkx4DzZ3LO19oebg ### 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)](#how-to-train-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: ##### 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` * Yolo v3 COCO - **image**: `darknet.exe detector test data/coco.data cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` * **Output coordinates** of objects: `darknet.exe detector test data/coco.data yolov3.cfg yolov3.weights -ext_output dog.jpg` * Yolo v3 COCO - **video**: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -ext_output test.mp4` * Yolo v3 COCO - **WebCam 0**: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights -c 0` * Yolo v3 COCO for **net-videocam** - Smart WebCam: `darknet.exe detector demo data/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 data/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25 test.mp4 -out_filename res.avi` * Yolo v3 **Tiny** COCO - video: `darknet.exe detector demo data/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 #0**: `darknet.exe detector demo data/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 0 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 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 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.txt` use: `darknet.exe detector test cfg/coco.data yolov3.cfg yolov3.weights -dont_show -ext_output < data/train.txt > result.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: 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 (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 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` 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: 1. If you have **MSVS 2015, 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\x***\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x***** 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://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg **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)). 1.1. Find files `opencv_world320.dll` and `opencv_ffmpeg320_***.dll` (or `opencv_world340.dll` and `opencv_ffmpeg340_***.dll`) in `C:\opencv_3.0\opencv\build\x***\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://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg * copy file `cudnn***_7.dll` to the folder `\build\darknet\x***` 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 do step 1 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\x***\vc14\lib`), then start MSVS, open `build\darknet\darknet_no_gpu.sln`, set **x***** 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 pathes 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\x***\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 that MSVS2015 had 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\x***\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x***;%(AdditionalLibraryDirectories)` - (right click on project) -> properties -> Linker -> Input -> Additional dependecies, put here: `..\..\3rdparty\lib\x***\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 (X*** & 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\x*** * `cusolver***_91.dll, curand***_91.dll, cudart***_91.dll, cublas***_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_***.dll` from `C:\opencv_3.0\opencv\build\x***\vc14\bin` * For OpenCV 2.4.13: `opencv_core2413.dll`, `opencv_highgui2413.dll` and `opencv_ffmpeg2413_***.dll` from `C:\opencv_2.4.13\opencv\build\x***\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\x***` 2. Download The Pascal VOC Data and unpack it to directory `build\darknet\x***\data\voc` will be created dir `build\darknet\x***\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\x***\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-amd***.exe 4. Run command: `python build\darknet\x***\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=***` and `subdivisions=8` in the file `yolov3-voc.cfg`: [link](https://github.com/AlexeyAB/darknet/blob/ee38c6e1513fb089b35be4ffa692afd9b3f65747/cfg/yolov3-voc.cfg#L3-L4) 7. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/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 pathes in the file `build\darknet\x***\data\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 data/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 data/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 c ... ...

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