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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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3
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7 cp -R /home/greg/_conda/envs/__darknet@1.0/bin/* . &&
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6
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8 darknet detect cfg/yolo.cfg yolo.weights '$input' -thresh $thresh 2> $output_log
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9 && mv ./predictions.png $output
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10 ]]></command>
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11 <inputs>
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7
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12 <param name="input" type="data" format="jpg,png" label="Select input image" />
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6
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13 <param name="thresh" type="float" value="0.25" label="Object detection threshold" />
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0
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14 </inputs>
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15 <outputs>
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16 <data name="output_log" format="txt" label="${tool.name} darknet output log) on ${on_string}" />
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17 <data name="output" format="png"/>
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18 </outputs>
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19 <tests>
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20 <test>
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21 </test>
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22 </tests>
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23 <help>
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24 **What it does**
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25
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26 You only look once (YOLO) is a state-of-the-art, real-time object detection system.
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27
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28 -----
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29
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30 **Options**
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31
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32 </help>
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33 <citations>
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34 <citation type="bibtex">
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35 @misc{darknet13,
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36 author = {Joseph Redmon},
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37 title = {Darknet: Open Source Neural Networks in C},
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38 url = {http://pjreddie.com/darknet/},
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39 year = {2013--2016}}
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40 </citation>
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41 <citation type="bibtex">
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42 @article{redmon2016yolo9000,
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43 title={YOLO9000: Better, Faster, Stronger},
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44 author={Redmon, Joseph and Farhadi, Ali},
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45 journal={arXiv preprint arXiv:1612.08242},
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46 year={2016}}
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47 </citation>
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48 </citations>
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49 </tool>
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