From bb48b9c992e2f48bff2432a0387681cad0c98dec Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Thu, 23 Aug 2018 00:00:48 +0000
Subject: [PATCH] Merge branch 'master' of github.com:AlexeyAB/darknet

---
 README.md |    8 ++++++--
 1 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 5eff175..18d3f15 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)
 
@@ -227,7 +227,9 @@
 
 1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg darknet53.conv.74`
 
-2. Then stop and by using partially-trained model `/backup/yolov3-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3`
+2. Adjust the learning rate (`cfg/yolov3-voc.cfg`) to fit the amount of GPUs. The learning rate should be equal to `0.001`, regardless of how many GPUs are used for training. So `learning_rate * GPUs = 0.001`. For 4 GPUs adjust the value to `learning_rate = 0.00025`.
+
+3. Then stop and by using partially-trained model `/backup/yolov3-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg /backup/yolov3-voc_1000.weights -gpus 0,1,2,3`
 
 https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ
 
@@ -488,6 +490,8 @@
 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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