From 07b39c5f57665cbc4e806c6dd4cac552e1179f35 Mon Sep 17 00:00:00 2001
From: PTS93 <timon@skerutsch.de>
Date: Sun, 28 Jan 2018 13:28:30 +0000
Subject: [PATCH] Fix autosave interval
---
README.md | 8 +++-----
1 files changed, 3 insertions(+), 5 deletions(-)
diff --git a/README.md b/README.md
index 8690ea6..aa540d0 100644
--- a/README.md
+++ b/README.md
@@ -166,8 +166,6 @@
`OPENCV;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)`
-- open file: `\src\detector.c` and check lines `#pragma` and `#inclue` for OpenCV.
-
- compile to .exe (X64 & Release) and put .dll-s near with .exe:
* `pthreadVC2.dll, pthreadGC2.dll` from \3rdparty\dll\x64
@@ -217,9 +215,9 @@
* change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.2.0.cfg#L2)
* change line subdivisions to [`subdivisions=8`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.2.0.cfg#L3)
* change line `classes=20` to your number of objects
- * change line #237 from [`filters=125`](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.2.0.cfg#L224) to: filters=(classes + 5)*5, so if `classes=2` then should be `filter=35`
+ * change line #237 from [`filters=125`](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.2.0.cfg#L224) to: filters=(classes + 5)x5, so if `classes=2` then should be `filters=35`. Or if you use `classes=1` then write `filters=30`, **do not write in the cfg-file: filters=(classes + 5)x5**.
- (Generally `filters` depends on the `classes`, `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`)
+ (Generally `filters` depends on the `classes`, `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`, where `num` is number of anchors)
So for example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolo-voc.2.0.cfg` in such lines:
@@ -273,7 +271,7 @@
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 until 1000 iterations has been reached, and after for each 1000 iterations)
+ (file `yolo-obj_xxx.weights` will be saved to the `build\darknet\x64\backup\` for each 100 iterations)
9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\`
--
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