<tool id="yolo" name="YOLO" version="1.0">
    <description>real-time object detection</description>
    <requirements>
        <requirement type="package" version="1.0">darknet</requirement>
    </requirements>
    <command detect_errors="exit_code"><![CDATA[
    cp -R /home/greg/_conda/envs/__darknet@1.0/bin/* . &&
darknet detect cfg/yolo.cfg yolo.weights '$input' -thresh $thresh 2> $output_log
&& mv ./predictions.png $output
    ]]></command>
    <inputs>
        <param name="input" type="data" format="jpg,png" label="Select input image" />
        <param name="thresh" type="float" value="0.25" label="Object detection threshold" />
    </inputs>
    <outputs>
        <data name="output_log" format="txt" label="${tool.name} darknet output log) on ${on_string}" />
        <data name="output" format="png"/>
    </outputs>
    <tests>
        <test>
        </test>
    </tests>
    <help>
**What it does**

You only look once (YOLO) is a state-of-the-art, real-time object detection system.

-----

**Options**

    </help>
    <citations>
        <citation type="bibtex">
            @misc{darknet13,
            author = {Joseph Redmon},
            title = {Darknet: Open Source Neural Networks in C},
            url = {http://pjreddie.com/darknet/},
            year = {2013--2016}}
        </citation>
        <citation type="bibtex">
            @article{redmon2016yolo9000,
            title={YOLO9000: Better, Faster, Stronger},
            author={Redmon, Joseph and Farhadi, Ali},
            journal={arXiv preprint arXiv:1612.08242},
            year={2016}}
        </citation>
    </citations>
</tool>