From a0dc4d717ab2d95e5e90f5b7b6344e8074b81606 Mon Sep 17 00:00:00 2001
From: Alexey <AlexeyAB@users.noreply.github.com>
Date: Fri, 30 Mar 2018 15:34:28 +0000
Subject: [PATCH] Update Readme.md

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

diff --git a/README.md b/README.md
index 0830e12..343db79 100644
--- a/README.md
+++ b/README.md
@@ -219,7 +219,7 @@
 
 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/yolo-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg /backup/yolo-voc_1000.weights -gpus 0,1,2,3`
+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`
 
 https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ
 
@@ -305,6 +305,8 @@
 
  * Also you can get result earlier than all 45000 iterations.
  
+ **Note:** If during training you see `nan` values in some lines then training goes well, but if `nan` are in all lines then training goes wrong.
+ 
 ### 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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