From 2f52cfeb07e11fce8275496164293cf30b732907 Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Fri, 16 Mar 2018 22:12:00 +0000
Subject: [PATCH] Fixed compile error

---
 README.md |    5 +++--
 1 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 21b6544..4e4cbb8 100644
--- a/README.md
+++ b/README.md
@@ -196,7 +196,7 @@
 
 6. Set `batch=64` and `subdivisions=8` in the file `yolo-voc.2.0.cfg`: [link](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.2.0.cfg#L2)
 
-7. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/voc.data yolo-voc.2.0.cfg darknet19_448.conv.23` (**Note:** If you are using CPU, try `darknet_no_gpu.exe` instead of `darknet.exe`.)
+7. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/voc.data yolo-voc.2.0.cfg darknet19_448.conv.23` (**Note:** To disable Loss-Window use flag `-dont_show`. If you are using CPU, try `darknet_no_gpu.exe` instead of `darknet.exe`.)
 
 If required change pathes in the file `build\darknet\x64\data\voc.data`
 
@@ -274,6 +274,7 @@
 8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23`
 
     (file `yolo-obj_xxx.weights` will be saved to the `build\darknet\x64\backup\` for each 100 iterations)
+    (To disable Loss-Window use `darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23 -dont_show`, if you train on computer without monitor like a cloud Amazaon EC2)
 
 9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\`
 
@@ -372,7 +373,7 @@
   
   * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides
 
-  * desirable that your training dataset include images with objects (without bounded boxes) that you do not want to detect - negative samples
+  * desirable that your training dataset include images with objects (without labels) that you do not want to detect - negative samples
 
   * for training on small objects, add the parameter `small_object=1` in the last layer [region] in your cfg-file
 

--
Gitblit v1.10.0