From f606b5456e4876da5f90e2902b2dff07516a03dc Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Wed, 22 Aug 2018 14:52:48 +0000
Subject: [PATCH] XNOR-net 21 FPS on CPU yolov2-tiny.cfg

---
 README.md |    4 +++-
 1 files changed, 3 insertions(+), 1 deletions(-)

diff --git a/README.md b/README.md
index 5eff175..963c401 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,5 @@
 # Yolo-v3 and Yolo-v2 for Windows and Linux
-### (neural network for object detection)
+### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows)
 
 [![CircleCI](https://circleci.com/gh/AlexeyAB/darknet.svg?style=svg)](https://circleci.com/gh/AlexeyAB/darknet)
 
@@ -488,6 +488,8 @@
 2. To use Yolo as DLL-file in your C++ console application - open in MSVS2015 file `build\darknet\yolo_console_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_console_dll
 
     * you can run your console application from Windows Explorer `build\darknet\x64\yolo_console_dll.exe`
+    **use this command**: `yolo_console_dll.exe data/coco.names yolov3.cfg yolov3.weights test.mp4`
+    
     * or you can run from MSVS2015 (before this - you should copy 2 files `yolo-voc.cfg` and `yolo-voc.weights` to the directory `build\darknet\` )
     * after launching your console application and entering the image file name - you will see info for each object: 
     `<obj_id> <left_x> <top_y> <width> <height> <probability>`

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