From 101de2b07aa2feefa74f7e73876fd5cc8fc696cf Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Tue, 20 Mar 2018 21:40:01 +0000
Subject: [PATCH] More stable web-cam
---
README.md | 17 ++++++++++-------
1 files changed, 10 insertions(+), 7 deletions(-)
diff --git a/README.md b/README.md
index 7b4e8c8..0877a9d 100644
--- a/README.md
+++ b/README.md
@@ -31,7 +31,7 @@
This repository supports:
* both Windows and Linux
-* both OpenCV 3.x and OpenCV 2.4.13
+* both OpenCV 2.x.x and OpenCV <= 3.4.0 (3.4.1 and higher isn't supported)
* both cuDNN v5-v7
* CUDA >= 7.5
* also create SO-library on Linux and DLL-library on Windows
@@ -39,7 +39,7 @@
##### Requires:
* **Linux GCC>=4.9 or Windows MS Visual Studio 2015 (v140)**: https://go.microsoft.com/fwlink/?LinkId=532606&clcid=0x409 (or offline [ISO image](https://go.microsoft.com/fwlink/?LinkId=615448&clcid=0x409))
* **CUDA 9.1**: https://developer.nvidia.com/cuda-downloads
-* **OpenCV 3.x**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.2.0/opencv-3.2.0-vc14.exe/download
+* **OpenCV 3.4.0**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.4.0/opencv-3.4.0-vc14_vc15.exe/download
* **or OpenCV 2.4.13**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.13/opencv-2.4.13.2-vc14.exe/download
- OpenCV allows to show image or video detection in the window and store result to file that specified in command line `-out_filename res.avi`
* **GPU with CC >= 2.0** if you use CUDA, or **GPU CC >= 3.0** if you use cuDNN + CUDA: https://en.wikipedia.org/wiki/CUDA#GPUs_supported
@@ -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:
@@ -196,7 +196,7 @@
6. Set `batch=64` and `subdivisions=8` in the file `yolo-voc.2.0.cfg`: [link](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.2.0.cfg#L2)
-7. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/voc.data yolo-voc.2.0.cfg darknet19_448.conv.23` (**Note:** If you are using CPU, try `darknet_no_gpu.exe` instead of `darknet.exe`.)
+7. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/voc.data yolo-voc.2.0.cfg darknet19_448.conv.23` (**Note:** To disable Loss-Window use flag `-dont_show`. If you are using CPU, try `darknet_no_gpu.exe` instead of `darknet.exe`.)
If required change pathes in the file `build\darknet\x64\data\voc.data`
@@ -274,6 +274,7 @@
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)
+ (To disable Loss-Window use `darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23 -dont_show`, if you train on computer without monitor like a cloud Amazaon EC2)
9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\`
@@ -369,10 +370,12 @@
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
- * for training on small objects, add the parameter `small_object=1` in the last layer [region] in your cfg-file
+ * desirable that your training dataset include images with objects (without labels) that you do not want to detect - negative samples
* 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
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