| | |
|---|
| | | # Yolo-Windows v2 |
|---|
| | | # Yolo-v2 Windows and Linux version |
|---|
| | | |
|---|
| | | 1. [How to use](#how-to-use) |
|---|
| | | 2. [How to compile](#how-to-compile) |
|---|
| | | 3. [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data) |
|---|
| | | 4. [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) |
|---|
| | | 5. [When should I stop training](#when-should-i-stop-training) |
|---|
| | | 6. [How to improve object detection](#how-to-improve-object-detection) |
|---|
| | | 7. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) |
|---|
| | | 8. [How to use Yolo as DLL](#how-to-use-yolo-as-dll) |
|---|
| | | 2. [How to compile on Linux](#how-to-compile-on-linux) |
|---|
| | | 3. [How to compile on Windows](#how-to-compile-on-windows) |
|---|
| | | 4. [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data) |
|---|
| | | 5. [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) |
|---|
| | | 6. [When should I stop training](#when-should-i-stop-training) |
|---|
| | | 7. [How to improve object detection](#how-to-improve-object-detection) |
|---|
| | | 8. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) |
|---|
| | | 9. [How to use Yolo as DLL](#how-to-use-yolo-as-dll) |
|---|
| | | |
|---|
| | | |  |  https://arxiv.org/abs/1612.08242 | |
|---|
| | | |---|---| |
|---|
| | |
|---|
| | | |
|---|
| | | |
|---|
| | | # "You Only Look Once: Unified, Real-Time Object Detection (version 2)" |
|---|
| | | A yolo windows version (for object detection) |
|---|
| | | |
|---|
| | | Contributtors: https://github.com/pjreddie/darknet/graphs/contributors |
|---|
| | | A Yolo cross-platform Windows and Linux version (for object detection). Contributtors: https://github.com/pjreddie/darknet/graphs/contributors |
|---|
| | | |
|---|
| | | This repository is forked from Linux-version: https://github.com/pjreddie/darknet |
|---|
| | | |
|---|
| | | More details: http://pjreddie.com/darknet/yolo/ |
|---|
| | | |
|---|
| | | This repository supports: |
|---|
| | | |
|---|
| | | * both Windows and Linux |
|---|
| | | * both OpenCV 3.x and OpenCV 2.4.13 |
|---|
| | | * both cuDNN 5 and cuDNN 6 |
|---|
| | | * CUDA >= 7.5 |
|---|
| | | * also create SO-library on Linux and DLL-library on Windows |
|---|
| | | |
|---|
| | | ##### Requires: |
|---|
| | | * **MS Visual Studio 2015 (v140)**: https://go.microsoft.com/fwlink/?LinkId=532606&clcid=0x409 (or offline [ISO image](https://go.microsoft.com/fwlink/?LinkId=615448&clcid=0x409)) |
|---|
| | | * **CUDA 8.0 for Windows x64**: https://developer.nvidia.com/cuda-downloads |
|---|
| | | * **OpenCV 3.0**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.2.0/opencv-3.2.0-vc14.exe/download |
|---|
| | | * **Linux GCC>=4.9 or Windows MS Visual Studio 2015 (v140)**: https://go.microsoft.com/fwlink/?LinkId=532606&clcid=0x409 (or offline [ISO image](https://go.microsoft.com/fwlink/?LinkId=615448&clcid=0x409)) |
|---|
| | | * **CUDA 8.0**: https://developer.nvidia.com/cuda-downloads |
|---|
| | | * **OpenCV 3.x**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.2.0/opencv-3.2.0-vc14.exe/download |
|---|
| | | * **or OpenCV 2.4.13**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.13/opencv-2.4.13.2-vc14.exe/download |
|---|
| | | - OpenCV allows to show image or video detection in the window and store result to file: test_dnn_out.avi |
|---|
| | | |
|---|
| | |
|---|
| | | * 256 MB COCO-model: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` |
|---|
| | | * 256 MB VOC-model: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` |
|---|
| | | |
|---|
| | | ### How to compile on Linux: |
|---|
| | | |
|---|
| | | ### How to compile: |
|---|
| | | Just do `make` in the darknet directory. |
|---|
| | | Before make, you can set such options in the `Makefile`: [link](https://github.com/AlexeyAB/darknet/blob/9c1b9a2cf6363546c152251be578a21f3c3caec6/Makefile#L1) |
|---|
| | | * `GPU=1` to build with CUDA to accelerate by using GPU |
|---|
| | | * `CUDNN=1` to build with cuDNN v5/v6 to accelerate training by using GPU |
|---|
| | | * `OPENCV=1` to build with OpenCV 3.x/2.4.x - allows to detect on video files and video streams from network cameras or web-cams |
|---|
| | | * `DEBUG=1` to bould debug version of Yolo |
|---|
| | | * `OPENMP=1` to build with OpenMP suuport to accelerate by using multi-core CPU |
|---|
| | | * `LIBSO=1` to build an library `darknet.so` and binary runable file `uselib` that uses this library. How to use this SO-library from your own code - you can look at C++ example: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp |
|---|
| | | |
|---|
| | | |
|---|
| | | ### How to compile on Windows: |
|---|
| | | |
|---|
| | | 1. If you have MSVS 2015, CUDA 8.0 and OpenCV 3.0 (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release**, and do the: Build -> Build darknet |
|---|
| | | |
|---|
| | |
|---|
| | | |
|---|
| | | 5. Run command: `type 2007_train.txt 2007_val.txt 2012_*.txt > train.txt` |
|---|
| | | |
|---|
| | | 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.cfg#L3) |
|---|
| | | 6. Set `batch=64` and `subdivisions=8` in the file `yolo-voc.2.0.cfg`: [link](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.2.0.cfg#L2) |
|---|
| | | |
|---|
| | | 7. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/voc.data yolo-voc.2.0.cfg darknet19_448.conv.23` |
|---|
| | | |
|---|
| | |
|---|
| | | |
|---|
| | | 1. Create file `yolo-obj.cfg` with the same content as in `yolo-voc.2.0.cfg` (or copy `yolo-voc.2.0.cfg` to `yolo-obj.cfg)` and: |
|---|
| | | |
|---|
| | | * change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.cfg#L3) |
|---|
| | | * change line subdivisions to [`subdivisions=8`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.cfg#L4) |
|---|
| | | * change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.2.0.cfg#L2) |
|---|
| | | * change line subdivisions to [`subdivisions=8`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.2.0.cfg#L3) |
|---|
| | | * change line `classes=20` to your number of objects |
|---|
| | | * change line #237 from [`filters=125`](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.cfg#L237) to `filters=(classes + 5)*5` (generally this depends on the `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`) |
|---|
| | | * change line #237 from [`filters=125`](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.2.0.cfg#L224) to `filters=(classes + 5)*5` (generally this depends on the `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`) |
|---|
| | | |
|---|
| | | For example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolo-voc.2.0.cfg` in such lines: |
|---|
| | | |
|---|
| | |
|---|
| | | ## How to improve object detection: |
|---|
| | | |
|---|
| | | 1. Before training: |
|---|
| | | * set flag `random=1` in your `.cfg`-file - it will increase precision by training Yolo for different resolutions: [link](https://github.com/AlexeyAB/darknet/blob/47409529d0eb935fa7bafbe2b3484431117269f5/cfg/yolo-voc.cfg#L244) |
|---|
| | | * set flag `random=1` in your `.cfg`-file - it will increase precision by training Yolo for different resolutions: [link]https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.2.0.cfg#L244) |
|---|
| | | |
|---|
| | | * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides |
|---|
| | | |
|---|
| | | 2. After training - for detection: |
|---|
| | | |
|---|
| | | * Increase network-resolution by set in your `.cfg`-file (`height=608` and `width=608`) or (`height=832` and `width=832`) or (any value multiple of 32) - this increases the precision and makes it possible to detect small objects: [link](https://github.com/AlexeyAB/darknet/blob/47409529d0eb935fa7bafbe2b3484431117269f5/cfg/yolo-voc.cfg#L4) |
|---|
| | | * Increase network-resolution by set in your `.cfg`-file (`height=608` and `width=608`) or (`height=832` and `width=832`) or (any value multiple of 32) - this increases the precision and makes it possible to detect small objects: [link](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.2.0.cfg#L4) |
|---|
| | | |
|---|
| | | * you do not need to train the network again, just use `.weights`-file already trained for 416x416 resolution |
|---|
| | | * if error `Out of memory` occurs then in `.cfg`-file you should increase `subdivisions=16`, 32 or 64: [link](https://github.com/AlexeyAB/darknet/blob/47409529d0eb935fa7bafbe2b3484431117269f5/cfg/yolo-voc.cfg#L3) |
|---|
| | | * if error `Out of memory` occurs then in `.cfg`-file you should increase `subdivisions=16`, 32 or 64: [link](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.2.0.cfg#L3) |
|---|
| | | |
|---|
| | | ## How to mark bounded boxes of objects and create annotation files: |
|---|
| | | |
|---|
| | |
|---|
| | | * to use simple OpenCV-GUI you should uncomment line `//#define OPENCV` in `yolo_console_dll.cpp`-file: [link](https://github.com/AlexeyAB/darknet/blob/a6cbaeecde40f91ddc3ea09aa26a03ab5bbf8ba8/src/yolo_console_dll.cpp#L5) |
|---|
| | | * you can see source code of simple example for detection on the video file: [link](https://github.com/AlexeyAB/darknet/blob/ab1c5f9e57b4175f29a6ef39e7e68987d3e98704/src/yolo_console_dll.cpp#L75) |
|---|
| | | |
|---|
| | | `yolo_cpp_dll.dll`-API: [link](https://github.com/AlexeyAB/darknet/blob/master/src/yolo_v2_class.hpp#L31) |
|---|
| | | `yolo_cpp_dll.dll`-API: [link](https://github.com/AlexeyAB/darknet/blob/master/src/yolo_v2_class.hpp#L42) |
|---|
| | | ``` |
|---|
| | | class Detector { |
|---|
| | | public: |
|---|
| | | Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0); |
|---|
| | | ~Detector(); |
|---|
| | | |
|---|
| | | std::vector<bbox_t> detect(std::string image_filename, float thresh = 0.2); |
|---|
| | | std::vector<bbox_t> detect(image_t img, float thresh = 0.2); |
|---|
| | | std::vector<bbox_t> detect(std::string image_filename, float thresh = 0.2, bool use_mean = false); |
|---|
| | | std::vector<bbox_t> detect(image_t img, float thresh = 0.2, bool use_mean = false); |
|---|
| | | static image_t load_image(std::string image_filename); |
|---|
| | | static void free_image(image_t m); |
|---|
| | | |
|---|
| | | #ifdef OPENCV |
|---|
| | | std::vector<bbox_t> detect(cv::Mat mat, float thresh = 0.2); |
|---|
| | | std::vector<bbox_t> detect(cv::Mat mat, float thresh = 0.2, bool use_mean = false); |
|---|
| | | #endif |
|---|
| | | }; |
|---|
| | | ``` |
|---|
