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