From 57678932921a813531d317c4b079a46f815e130d Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Thu, 19 Apr 2018 22:59:04 +0000
Subject: [PATCH] Fixed typo for calc_anchors

---
 README.md |    9 +++++++--
 1 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 3eeda63..6b98f50 100644
--- a/README.md
+++ b/README.md
@@ -125,6 +125,7 @@
 Before make, you can set such options in the `Makefile`: [link](https://github.com/AlexeyAB/darknet/blob/9c1b9a2cf6363546c152251be578a21f3c3caec6/Makefile#L1)
 * `GPU=1` to build with CUDA to accelerate by using GPU (CUDA should be in `/usr/local/cuda`)
 * `CUDNN=1` to build with cuDNN v5-v7 to accelerate training by using GPU (cuDNN should be in `/usr/local/cudnn`)
+* `CUDNN_HALF=1` to build for Tensor Cores (on Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x
 * `OPENCV=1` to build with OpenCV 3.x/2.4.x - allows to detect on video files and video streams from network cameras or web-cams
 * `DEBUG=1` to bould debug version of Yolo
 * `OPENMP=1` to build with OpenMP support to accelerate Yolo by using multi-core CPU
@@ -157,6 +158,8 @@
   
     4.2 (right click on project) -> properties  -> Linker -> General -> Additional Library Directories: `C:\opencv_2.4.13\opencv\build\x64\vc14\lib`
     
+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;`
 
 ### How to compile (custom):
 
@@ -227,7 +230,9 @@
 https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ
 
 ## How to train (to detect your custom objects):
-Training Yolo v3
+(to train old Yolo v2 `yolov2-voc.cfg`, `yolov2-tiny-voc.cfg`, `yolo-voc.cfg`, `yolo-voc.2.0.cfg`, ... [click by the link](https://github.com/AlexeyAB/darknet/tree/47c7af1cea5bbdedf1184963355e6418cb8b1b4f#how-to-train-pascal-voc-data))
+
+Training Yolo v3:
 
 1. Create file `yolo-obj.cfg` with the same content as in `yolov3.cfg` (or copy `yolov3.cfg` to `yolo-obj.cfg)` and:
 
@@ -408,7 +413,7 @@
   * increase network resolution in your `.cfg`-file (`height=608`, `width=608` or any value multiple of 32) - it will increase precision
 
   * recalculate anchors for your dataset for `width` and `height` from cfg-file:
-  `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -heigh 416`
+  `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416`
    then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file
 
   * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds

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