From f0bc634a74b14ce4b0e065011390940d4d7a254c Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Mon, 05 Mar 2018 10:25:27 +0000
Subject: [PATCH] Fixed saving video file

---
 README.md |    5 ++++-
 1 files changed, 4 insertions(+), 1 deletions(-)

diff --git a/README.md b/README.md
index 2f75fb3..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\`
 
@@ -372,6 +373,8 @@
   
   * 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 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
 
   * for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last layer [region] in your cfg-file

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