From 0b4b2753bf3a02553c05d9ba2d31eba262e5c29e Mon Sep 17 00:00:00 2001 From: Alexey <AlexeyAB@users.noreply.github.com> Date: Tue, 31 Jan 2017 10:29:55 +0000 Subject: [PATCH] Update Readme.md --- README.md | 13 ++++++++++--- 1 files changed, 10 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 7b7abdb..3fd4d10 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) @@ -76,7 +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. 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 @@ -192,7 +199,7 @@ 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)` -- Gitblit v1.10.0