The YOLO algorithm framework uses two

6,VOC Data Set Training model

1) Download Data set

The website provides some VOC data, which is based on 2007 to 2012, and you can download it at the following address:

wget https://pjreddie.com/media/files/voctrainval_11-may-2012.tarwget https: // Pjreddie.com/media/files/voctrainval_06-nov-2007.tar wget https://pjreddie.com/media/files/voctest_06-nov-2007.tartar XF voctrainval_11-may-. Tar tar XF voctrainval_06-nov-. Tar tar XF voctest_06-nov-. Tar

The data can be stored in the vocdevkit/directory

2) Generate Identification tags

The identification tag must be a. txt file in the following format:

<object-class> <x> <y> <width> 
Object-class is the name of the category
The remaining elements are pixels associated to the picture, and the width and height of the
By downloading the voc_label.py available on the website we can quickly generate this file and download it to the scripts/directory:
wget https://pjreddie.com/media/files/voc_label.pypython voc_label.py
After a few minutes, the corresponding file is generated to be stored in:
vocdevkit/voc2007/labels/or vocdevkit/voc2012/labels/below:
ls 2007_test.txt   Vocdevkit2007_train.txt  voc_label.py2007_val.txt    voctest_06-nov-.  Tar2012_train.txt  voctrainval_06-nov-.  Tar2012_val.txt    voctrainval_11-may-.  Tar
We can combine the files we really want to train into one:
cat 2007_train.txt 2007_val.txt 2012_*.txt > Train.txt
3) Modify the data that the configuration points to (Pascal data)
In the Cfg/voc.data, point to the configuration data:
1  -  2 Train  = <path-to-voc>/train.txt  3 valid  = <path-to-voc>  2007_test.txt  4 names = data/voc.names  5 backup = Backup
<path-to-voc> is the point of your data set
4) Download weights for pre-trained convolution
The weight of the convolution here is imagenet pre-training provides:
wget https://pjreddie.com/media/files/darknet19_448.conv.23
You can also generate your own weights by downloading the pre-trained Darknet19 448x448 model (https://pjreddie.com/darknet/imagenet/#darknet19_448), performing the following naming:
./darknet partial Cfg/darknet19_448.cfg darknet19_448.weights darknet19_448.conv.
5) Training Model
./darknet Detector Train Cfg/voc.data cfg/yolo-voc.cfg darknet19_448.conv.
7, using Coco to train YOLO model
Coco DataSet, I have not used, specifically can see http://cocodataset.org/#overview understand
1) Get Coco Data Set
Download Coco's datasets and tags, which can be executed directly from the scripts/get_coco_dataset.sh script:
CP Scripts/get_coco_dataset. SH DATACD databash get_coco_dataset. SH
So the tags and datasets are there.
2) pointing to the configuration data set
In the Cfg/coco.data configuration file, configure:
1  the  2 Train  = <path-to-coco>/trainvalno5k.txt  3 valid  = <path-to-coco >/5k.txt  4 names = data/coco.names  5 backup = Backup
<path-to-coco> is your specific path to pointing
Also need to configure your dataset to be used for training is not tested, default is the configuration of the test, in Cfg/yolo.cfg:
[net]# testing# Batch=1# subdivisions=1# trainingbatch= subdivisions=8....
3) Training Model
./darknet Detector Train Cfg/coco.data cfg/yolo.cfg darknet19_448.conv.
4) Enable GPUs to perform training and speed up
./darknet Detector Train Cfg/coco.data cfg/yolo.cfg darknet19_448.conv.0,1,  2,3
5) Training pauses or starts from breakpoints
0,1,2,3
8, the official special statement
If you use their frame, you must explain the source of the frame in the comments, and you can paste the following comment directly into the comment:
@article {redmon2016yolo9000,  title={yolo9000:better, Faster, stronger},  author={ Redmon, Joseph and Farhadi, Ali},  journal={arxiv preprint arXiv:1612.08242},  year ={}}
Reference Address: https://pjreddie.com/darknet/yolo/
Paper Address: https://arxiv.org/abs/1612.08242
The YOLO algorithm framework uses two