From 3e5abe0680c6112c9674204c22db7bd4b238d2b5 Mon Sep 17 00:00:00 2001
From: Alexey <AlexeyAB@users.noreply.github.com>
Date: Tue, 20 Mar 2018 00:14:20 +0000
Subject: [PATCH] Update Readme.md
---
README.md | 10 +++++-----
1 files changed, 5 insertions(+), 5 deletions(-)
diff --git a/README.md b/README.md
index 4e4cbb8..0877a9d 100644
--- a/README.md
+++ b/README.md
@@ -92,8 +92,8 @@
* 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data yolo9000.cfg yolo9000.weights`
* 186 MB Yolo9000 - video: `darknet.exe detector demo cfg/combine9k.data yolo9000.cfg yolo9000.weights test.mp4`
* Remeber to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app
-* To process a list of images `image_list.txt` and save results of detection to `result.txt` use:
- `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights < image_list.txt > result.txt`
+* To process a list of images `data/train.txt` and save results of detection to `result.txt` use:
+ `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -dont_show < data/train.txt > result.txt`
You can comment this line so that each image does not require pressing the button ESC: https://github.com/AlexeyAB/darknet/blob/6ccb41808caf753feea58ca9df79d6367dedc434/src/detector.c#L509
##### For using network video-camera mjpeg-stream with any Android smartphone:
@@ -370,13 +370,13 @@
1. Before training:
* set flag `random=1` in your `.cfg`-file - it will increase precision by training Yolo for different resolutions: [link]https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.2.0.cfg#L244)
-
+
+ * increase network resolution in your `.cfg`-file (`height=608`, `width=608` or any value multiple of 32) - it will increase precision
+
* desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides
* desirable that your training dataset include images with objects (without labels) that you do not want to detect - negative samples
- * for training on small objects, add the parameter `small_object=1` in the last layer [region] in your cfg-file
-
* for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last layer [region] in your cfg-file
* to speedup training (with decreasing detection accuracy) do Fine-Tuning instead of Transfer-Learning, set param `stopbackward=1` in one of the penultimate convolutional layers, for example here: https://github.com/AlexeyAB/darknet/blob/cad4d1618fee74471d335314cb77070fee951a42/cfg/yolo-voc.2.0.cfg#L202
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