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+is+a+solid+perception+system%252C+where+camera+encoders+play+a+crucial+role.+Existing+works+usually+leverage+pre-trained+Convolutional+Neural+Networks+(CNN)+or+Vision+Transformers+(ViTs)+designed+for+general+vision+tasks%252C+such+as+image+classification%252C+segmentation%252C+and+2D+detection.+Although+those+well-known+architectures+have+achieved+state-of-the-art+accuracy+in+AV-related+tasks%252C+e.g.%252C+3D+Object+Detection%252C+there+remains+significant+potential+for+improvement+in+network+design+due+to+the+nuanced+complexities+of+industrial-level+AV+dataset.+Moreover%252C+existing+public+AV+benchmarks+usually+contain+insufficient+data%252C+which+might+lead+to+inaccurate+evaluation+of+those+architectures.To+reveal+the+AV-specific+model+insights%252C+we+start+from+a+standard+general-purpose+encoder%252C+ConvNeXt+and+progressively+transform+the+design.+We+adjust+different+design+parameters+including+width+and+depth+of+the+model%252C+stage+compute+ratio%252C+attention+mechanisms%252C+and+input+resolution%252C+supported+by+systematic+analysis+to+each+modifications.+This+customization+yields+an+architecture+optimized+for+AV+camera+encoder+achieving+8.79%2525+mAP+improvement+over+the+baseline.+We+believe+our+effort+could+become+a+sweet+cookbook+of+image+encoders+for+AV+and+pave+the+way+to+the+next-level+drive+system.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.9c9deabfe537e574f772ebf4d1b6b798%3frik%3dko5FStkxdwKkvg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Autonomous+Car+Camera+Input&FORM=IRPRST&ck=9007845C15295E83E89958489887F782&selectedIndex=6&itb=0)
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