From 63aeb63dee51aff8d0b7c862f9c7966e055eb061 Mon Sep 17 00:00:00 2001
From: Alexey <AlexeyAB@users.noreply.github.com>
Date: Thu, 07 Jun 2018 13:46:23 +0000
Subject: [PATCH] Update Readme.md

---
 README.md |    6 ++++--
 1 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 7cf0b77..23688af 100644
--- a/README.md
+++ b/README.md
@@ -159,6 +159,8 @@
     
 5. If you have GPU with Tensor Cores (nVidia Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x:
     `\darknet.sln` -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add here: `CUDNN_HALF;`
+    
+    **Note:** CUDA must be installed only after that MSVS2015 had been installed.
 
 ### How to compile (custom):
 
@@ -180,7 +182,7 @@
 
 `OPENCV;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)`
 
-- compile to .exe (X64 & Release) and put .dll-s near with .exe:
+- compile to .exe (X64 & Release) and put .dll-s near with .exe: https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg
 
     * `pthreadVC2.dll, pthreadGC2.dll` from \3rdparty\dll\x64
 
@@ -417,7 +419,7 @@
 
   * check that each object are mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark
 
-  * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds
+  * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 images for each class or more
 
   * desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty `.txt` files)
 

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