Auxilary scripts to work with (YOLO) darknet deep learning famework. AKA -> How to generate YOLO anchors?
Auxilary scripts for working with darknet deep learning famework (2017)
Download The Pascal VOC Data and unpack it to directory build\darknet\x64\data\voc
will be created dir build\darknet\x64\data\voc\VOCdevkit\
:
1.1 Download file voc_label.py
to dir build\darknet\x64\data\voc
: http://pjreddie.com/media/files/voc_label.py
Download and install Python for Windows: https://www.python.org/ftp/python/2.7.9/python-2.7.9rc1.amd64.msi
Run command: python build\darknet\x64\data\voc\voc_label.py
(to generate files: 2007_test.txt, 2007_train.txt, 2007_val.txt, 2012_train.txt, 2012_val.txt)
Run command: type 2007_train.txt 2007_val.txt 2012_*.txt > train.txt
Obtain anchors5.txt in generated_anchors/voc-reproduce folder by executing:
python gen_anchors.py -filelist //path//to//voc//filelist/list//train.txt -output_dir generated_anchors/voc-reproduce -num_clusters 5
After completing the steps above, execute
python visualize_anchors.py -anchor_dir generated_anchors/voc-reproduce
Simply change the lines here https://github.com/Jumabek/darknet_scripts/blob/master/gen_anchors.py#L17 to your input dimension. Then compute the anchors.
In order to plot a loss, you first need a log of the darknet train command
For example,below command will save the log into log/aggregate-voc-tiny7.log
darknet.exe detector train data/aggregate-voc-tiny7.data cfg/aggregate-voc-tiny7.cfg backup/aggregate-voc-tiny7/aggregate-voc-tiny7_21000.weights >> log/aggregate-voc-tiny7.log -gpus 0,1
Once you have \path\to\log\aggregate-voc-tiny7.log, plot the loss by executing
python plot_yolo_log.py \\path\\to\\log\\aggregate-voc-tiny7.log