From 76dbdae388a6c269cbf46d28e53fee8ce4ace94d Mon Sep 17 00:00:00 2001 From: Alexey <AlexeyAB@users.noreply.github.com> Date: Tue, 14 Feb 2017 21:28:16 +0000 Subject: [PATCH] Update Readme.md --- README.md | 48 +++++++++++++++++++++++++++++++----------------- 1 files changed, 31 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index 72075c9..d4f850e 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,15 @@ +# Yolo-Windows v2 + +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. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) + |  |  https://arxiv.org/abs/1612.08242 | |---|---| -# Yolo-Windows v2 # "You Only Look Once: Unified, Real-Time Object Detection (version 2)" A yolo windows version (for object detection) @@ -62,8 +69,8 @@ 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.SmartWebCam - IP Webcam: https://play.google.com/store/apps/details?id=com.pas.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 @@ -76,16 +83,7 @@ ### How to compile: -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\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release**, and do the: Build -> Build darknet - - 1.1 If you want to build with CUDNN to speed up, then: - - * download and install CUDNN: https://developer.nvidia.com/cudnn - - * add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg - - * open `\darknet.sln` -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add at the beginning of line: `CUDNN;` - +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 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 @@ -104,6 +102,14 @@ 4. If you have other version of OpenCV 3.x (not 2.4.x) then you should change many places in code by yourself. +5. If you want to build with CUDNN to speed up then: + + * download and install CUDNN: https://developer.nvidia.com/cudnn + + * add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg + + * open `\darknet.sln` -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add at the beginning of line: `CUDNN;` + ### How to compile (custom): Also, you can to create your own `darknet.sln` & `darknet.vcxproj`, this example for CUDA 8.0 and OpenCV 2.4.9 @@ -140,7 +146,12 @@ 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` -2. Download The Pascal VOC Data and unpack it to directory `build\darknet\x64\data\voc`: http://pjreddie.com/projects/pascal-voc-dataset-mirror/ will be created file `voc_label.py` and `\VOCdevkit\` dir +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\`: + * 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\x64\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-amd64.exe @@ -167,7 +178,7 @@ 1. Create file `yolo-obj.cfg` with the same content as in `yolo-voc.cfg` (or copy `yolo-voc.cfg` to `yolo-obj.cfg)` and: * change line `classes=20` to your number of objects - * change line `filters=425` to `filters=(classes + 5)*5` (generally this depends on the `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`) + * change line #224 from [`filters=125`](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.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.cfg` in such lines: @@ -193,11 +204,12 @@ 4. Put image-files (.jpg) of your objects in the directory `build\darknet\x64\data\obj\` -5. Create `.txt`-file for each `.jpg`-image-file - 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>` +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>` Where: * `<object-class>` - integer number of object from `0` to `(classes-1)` - * `<x> <y> <width> <height>` - float values relative to width and height of image, it can be equal from 0.0 to 1.0 + * `<x> <y> <width> <height>` - float values relative to width and height of image, it can be equal from 0.0 to 1.0 + * for example: `<x> = <absolute_x> / <image_width>` or `<height> = <absolute_height> / <image_height>` * atention: `<x> <y>` - are center of rectangle (are not top-left corner) For example for `img1.jpg` you should create `img1.txt` containing: @@ -222,6 +234,8 @@ 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, 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. ### Custom object detection: -- Gitblit v1.10.0