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Optimizing one's observing strategy while at the telescope relies on knowingthe current observing conditions and the obtained data quality. In particularthe latter is not straight forward with current wide-field imagers, such as theWIYN One Degree Imager (ODI), currently consisting of 13 detectors, each ofthem read out in 64 independent cells.  Here we present a fast data reduction software for ODI, optimized for a firstdata inspection during acquisition at the the telescope, but capable enough forscience-quality data reductions. The pipeline is coded in pure python withminimal additional requirements. It is installed on the ODI observer'sinterface and publicly available from the author's webpage. It performs allbasic reduction steps as well as more advanced corrections for pupil-ghostremoval, fringe correction and masking of persistent pixels. Additionalcapabilities include adding an accurate astrometric WCS solution based on the2MASS reference system as well as photometric zeropoint calibration for framescovered by the SDSS foot-print.  The pipeline makes use of multiple CPU-cores wherever possible, resulting inan execution time of only a few seconds per frame. As such this QuickReducepipeline offers the ODI observer a convenient way to closely monitor dataquality, a necessity to optimize the observing strategy during the night.

The QuickReduce data reduction pipeline for the WIYN One Degree Imager