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1 <tool id="yolo" name="YOLO" version="1.0">
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2 <description>real-time object detection</description>
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3 <requirements>
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4 <requirement type="package" version="1.0">darknet</requirement>
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5 </requirements>
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6 <command detect_errors="exit_code"><![CDATA[
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7 cp -R /home/greg/_conda/envs/__darknet@1.0/bin/* . &&
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8 darknet detect cfg/yolo.cfg yolo.weights '$input' 2> output.log
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9 && mv ./predictions.png $output
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10 ]]></command>
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11 <inputs>
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12 <param name="input" type="data" format="jpg" label="Select input image" />
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13 </inputs>
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14 <outputs>
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15 <data name="output_log" format="txt" label="${tool.name} darknet output log) on ${on_string}" />
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16 <data name="output" format="png"/>
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17 </outputs>
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18 <tests>
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19 <test>
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20 </test>
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21 </tests>
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22 <help>
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23 **What it does**
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24
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25 You only look once (YOLO) is a state-of-the-art, real-time object detection system.
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26
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27 -----
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28
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29 **Options**
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30
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31 </help>
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32 <citations>
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33 <citation type="bibtex">
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34 @misc{darknet13,
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35 author = {Joseph Redmon},
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36 title = {Darknet: Open Source Neural Networks in C},
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37 url = {http://pjreddie.com/darknet/},
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38 year = {2013--2016}}
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39 </citation>
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40 <citation type="bibtex">
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41 @article{redmon2016yolo9000,
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42 title={YOLO9000: Better, Faster, Stronger},
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43 author={Redmon, Joseph and Farhadi, Ali},
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44 journal={arXiv preprint arXiv:1612.08242},
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45 year={2016}}
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46 </citation>
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47 </citations>
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48 </tool>
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