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| | | * **CUDA 8.0**: https://developer.nvidia.com/cuda-downloads |
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| | | * **OpenCV 3.x**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.2.0/opencv-3.2.0-vc14.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: test_dnn_out.avi |
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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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| | | |
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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` (256 MB COCO-model) - require 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo.weights |
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| | | * `yolo-voc.cfg` (256 MB VOC-model) - require 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo-voc.weights |
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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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| | | |
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| | | Put it near compiled: darknet.exe |
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| | | |
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| | |
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| | | ##### Example of usage in cmd-files from `build\darknet\x64\`: |
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| | | |
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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, and store result to: test_dnn_out.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) and store result to: test_dnn_out.avi |
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| | | * `darknet_web_cam_voc.cmd` - initialization with 194 MB VOC-model, play video from Web-Camera number #0 and store result to: test_dnn_out.avi |
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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 |
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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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| | | |
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| | | ##### How to use on the command line: |
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| | | |
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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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| | | |
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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 256 MB COCO-model - image: `darknet.exe detect 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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| | | * Alternative method 256 MB VOC-model - video: `darknet.exe yolo demo 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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| | | 4. Replace the address below, on shown in the phone application (Smart WebCam) and launch: |
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| | | |
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| | | |
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| | | * 256 MB COCO-model: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` |
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| | | * 256 MB VOC-model: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` |
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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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| | | |
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| | | ### How to compile on Linux: |
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| | | |
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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 |
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| | | * `CUDNN=1` to build with cuDNN v5/v6 to accelerate training by using GPU |
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| | | * `GPU=1` to build with CUDA to accelerate by using GPU (CUDA should be in `/use/local/cuda`) |
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| | | * `CUDNN=1` to build with cuDNN v5/v6 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 suuport to accelerate by using multi-core CPU |
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| | | * `LIBSO=1` to build an library `darknet.so` and binary runable file `uselib` that uses this library. How to use this SO-library from your own code - you can look at C++ example: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp |
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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. 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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| | | |
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| | | ### How to compile on Windows: |
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| | | |
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| | | 1. If you have MSVS 2015, CUDA 8.0 and OpenCV 3.0 (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release**, and do the: Build -> Build darknet |
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| | | 1. If you have **MSVS 2015, CUDA 8.0 and OpenCV 3.0** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release**, and do the: Build -> Build darknet |
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| | | |
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| | | 1.1. Find files `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` in `C:\opencv_3.0\opencv\build\x64\vc14\bin` and put it near with `darknet.exe` |
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| | | 1.1. Find files `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` in `C:\opencv_3.0\opencv\build\x64\vc14\bin` and put it near with `darknet.exe` |
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| | | |
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| | | 2. If you have other version of CUDA (not 8.0) then open `build\darknet\darknet.vcxproj` by using Notepad, find 2 places with "CUDA 8.0" and change it to your CUDA-version, then do step 1 |
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| | | 2. If you have other version of **CUDA (not 8.0)** then open `build\darknet\darknet.vcxproj` by using Notepad, find 2 places with "CUDA 8.0" and change it to your CUDA-version, then do step 1 |
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| | | |
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| | | 3. 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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| | | 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 |
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| | | 3.1 (right click on project) -> properties -> C/C++ -> General -> Additional Include Directories: `C:\opencv_2.4.13\opencv\build\include` |
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| | | 4. 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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| | | |
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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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| | | 3.2 (right click on project) -> properties -> Linker -> General -> Additional Library Directories: `C:\opencv_2.4.13\opencv\build\x64\vc14\lib` |
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| | | 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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| | | 4. If you have other version of OpenCV 2.4.x (not 3.x) then you also should change lines like `#pragma comment(lib, "opencv_core2413.lib")` in the file `\src\detector.c` |
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| | | 5. If you have other version of OpenCV 2.4.x (not 3.x) then you also should change lines like `#pragma comment(lib, "opencv_core2413.lib")` in the file `\src\detector.c` |
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| | | 5. If you want to build with CUDNN to speed up then: |
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| | | 6. If you want to build with CUDNN to speed up then: |
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| | | * download and install **cuDNN 6.0 for CUDA 8.0**: https://developer.nvidia.com/cudnn |
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