From 5fc1e9a918bfb9229eec90d459dbfb6778ae6906 Mon Sep 17 00:00:00 2001
From: Sam Beran <sberan@gmail.com>
Date: Tue, 10 Jul 2018 22:13:00 +0000
Subject: [PATCH] ext_output: flush stdout after printing output

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

diff --git a/README.md b/README.md
index 89b053f..b02da01 100644
--- a/README.md
+++ b/README.md
@@ -282,7 +282,7 @@
 It will 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)`
+  * `<object-class>` - integer object number from `0` to `(classes-1)`
   * `<x> <y> <width> <height>` - float values relative to width and height of image, it can be equal from (0.0 to 1.0]
   * for example: `<x> = <absolute_x> / <image_width>` or `<height> = <absolute_height> / <image_height>`
   * atention: `<x> <y>` - are center of rectangle (are not top-left corner)
@@ -324,6 +324,8 @@
  
  **Note:** After training use such command for detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights`
  
+  **Note:** if error `Out of memory` occurs then in `.cfg`-file you should increase `subdivisions=16`, 32 or 64: [link](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4)
+ 
 ### How to train tiny-yolo (to detect your custom objects):
 
 Do all the same steps as for the full yolo model as described above. With the exception of:

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