From be90b8e8cb6bbf3951a5e185aa43ccfdd4a03f4d Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Thu, 08 Feb 2018 22:50:35 +0000
Subject: [PATCH] Optimal params for optical flow tracking. Some small box fixes.
---
README.md | 5 ++++-
1 files changed, 4 insertions(+), 1 deletions(-)
diff --git a/README.md b/README.md
index 34aa5b5..ebe9a1a 100644
--- a/README.md
+++ b/README.md
@@ -90,6 +90,7 @@
* 194 MB VOC-model - WebCamera #0: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights -c 0`
* 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`
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
@@ -271,7 +272,7 @@
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\`
@@ -340,6 +341,8 @@
* desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides
+ * for training on small objects, add the parameter `small_object=1` in the last layer [region] in your cfg-file
+
2. After training - for detection:
* Increase network-resolution by set in your `.cfg`-file (`height=608` and `width=608`) or (`height=832` and `width=832`) or (any value multiple of 32) - this increases the precision and makes it possible to detect small objects: [link](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.2.0.cfg#L4)
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