From b9b0bf5131940217f83a49ec2b107edc645ca7ac Mon Sep 17 00:00:00 2001
From: Alexey <AlexeyAB@users.noreply.github.com>
Date: Fri, 13 Apr 2018 11:07:51 +0000
Subject: [PATCH] Update Readme.md
---
README.md | 18 ++++++++++--------
1 files changed, 10 insertions(+), 8 deletions(-)
diff --git a/README.md b/README.md
index e6b576a..fa2809b 100644
--- a/README.md
+++ b/README.md
@@ -46,7 +46,7 @@
* **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
+* **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported
##### Pre-trained models for different cfg-files can be downloaded from (smaller -> faster & lower quality):
* `yolov3.cfg` (236 MB COCO **Yolo v3**) - require 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3.weights
@@ -167,7 +167,7 @@
`C:\opencv_3.0\opencv\build\include;..\..\3rdparty\include;%(AdditionalIncludeDirectories);$(CudaToolkitIncludeDir);$(cudnn)\include`
- (right click on project) -> Build dependecies -> Build Customizations -> set check on CUDA 9.1 or what version you have - for example as here: http://devblogs.nvidia.com/parallelforall/wp-content/uploads/2015/01/VS2013-R-5.jpg
-- add to project all .c & .cu files from `\src`
+- add to project all `.c` & `.cu` files and file `http_stream.cpp` from `\src`
- (right click on project) -> properties -> Linker -> General -> Additional Library Directories, put here:
`C:\opencv_3.0\opencv\build\x64\vc14\lib;$(CUDA_PATH)lib\$(PlatformName);$(cudnn)\lib\x64;%(AdditionalLibraryDirectories)`
@@ -216,6 +216,8 @@
More information about training by the link: http://pjreddie.com/darknet/yolo/#train-voc
+ **Note:** If during training you see `nan` values in some lines then training goes well, but if `nan` are in all lines then training goes wrong.
+
## How to train with multi-GPU:
1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data cfg/yolov3-voc.cfg darknet53.conv.74`
@@ -314,10 +316,10 @@
### How to train tiny-yolo (to detect your custom objects):
Do all the same steps as for the full yolo model as described above. With the exception of:
-* Download default weights file for tiny-yolo-voc: http://pjreddie.com/media/files/tiny-yolo-voc.weights
-* Get pre-trained weights tiny-yolo-voc.conv.13 using command: `darknet.exe partial cfg/tiny-yolo-voc.cfg tiny-yolo-voc.weights tiny-yolo-voc.conv.13 13`
-* Make your custom model `tiny-yolo-obj.cfg` based on `tiny-yolo-voc.cfg` instead of `yolo-voc.2.0.cfg`
-* Start training: `darknet.exe detector train data/obj.data tiny-yolo-obj.cfg tiny-yolo-voc.conv.13`
+* Download default weights file for yolov2-tiny-voc: http://pjreddie.com/media/files/yolov2-tiny-voc.weights
+* Get pre-trained weights yolov2-tiny-voc.conv.13 using command: `darknet.exe partial cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights yolov2-tiny-voc.conv.13 13`
+* Make your custom model `yolov2-tiny-obj.cfg` based on `cfg/yolov2-tiny-voc.cfg` instead of `yolov3.cfg`
+* Start training: `darknet.exe detector train data/obj.data yolov2-tiny-obj.cfg yolov2-tiny-voc.conv.13`
For training Yolo based on other models ([DenseNet201-Yolo](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/densenet201_yolo.cfg) or [ResNet50-Yolo](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/resnet50_yolo.cfg)), you can download and get pre-trained weights as showed in this file: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/partial.cmd
If you made you custom model that isn't based on other models, then you can train it without pre-trained weights, then will be used random initial weights.
@@ -407,9 +409,9 @@
`darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -heigh 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
+ * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds
- * desirable that your training dataset include images with objects (without labels) that you do not want to detect - negative samples
+ * desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box
* 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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