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| | | # Yolo-Windows v2 |
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| | | 1. [How to use](#how-to-use) |
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| | | 2. [How to compile](#how-to-compile) |
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| | | 3. [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data) |
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| | | 4. [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) |
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| | | 5. [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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| | | |  |  https://arxiv.org/abs/1612.08242 | |
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| | | # Yolo-Windows v2 |
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| | | # "You Only Look Once: Unified, Real-Time Object Detection (version 2)" |
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| | | A yolo windows version (for object detection) |
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| | | ### How to compile: |
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| | | 1. If you have CUDA 8.0, OpenCV 2.4.9 (C:\opencv_2.4.9) and MSVS 2015 then start MSVS, open `build\darknet\darknet.sln` and do the: Build -> Build darknet |
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| | | 1. If you have MSVS 2015, CUDA 8.0 and OpenCV 2.4.9 (with paths: `C:\opencv_2.4.9\opencv\build\include` & `C:\opencv_2.4.9\opencv\build\x64\vc12\lib` or `vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release**, and do the: Build -> Build darknet |
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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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| | | 4. If you have other version of OpenCV 3.x (not 2.4.x) then you should change many places in code by yourself. |
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| | | 5. If you want to build with CUDNN to speed up then: |
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| | | * download and install CUDNN: https://developer.nvidia.com/cudnn |
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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 8.0 and OpenCV 2.4.9 |
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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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| | | * `<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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| | | 8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23` |
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| | | 9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\` |
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| | | * 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` |
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| | | * Also you can get result earlier than all 45000 iterations, for example, usually sufficient 2000 iterations for each class(object). I.e. for 6 classes to avoid overfitting - you can stop training after 12000 iterations and use `yolo-obj_12000.weights` to detection. |
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| | | ### Custom object detection: |
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| | | Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_3000.weights` |
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| | | ## How to mark bounded boxes of objects and create annotation files: |
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