%20is%20a%20solid%20perception%20system%2C%20where%20camera%20encoders%20play%20a%20crucial%20role.%20Existing%20works%20usually%20leverage%20pre-trained%20Convolutional%20Neural%20Networks%20(CNN)%20or%20Vision%20Transformers%20(ViTs)%20designed%20for%20general%20vision%20tasks%2C%20such%20as%20image%20classification%2C%20segmentation%2C%20and%202D%20detection.%20Although%20those%20well-known%20architectures%20have%20achieved%20state-of-the-art%20accuracy%20in%20AV-related%20tasks%2C%20e.g.%2C%203D%20Object%20Detection%2C%20there%20remains%20significant%20potential%20for%20improvement%20in%20network%20design%20due%20to%20the%20nuanced%20complexities%20of%20industrial-level%20AV%20dataset.%20Moreover%2C%20existing%20public%20AV%20benchmarks%20usually%20contain%20insufficient%20data%2C%20which%20might%20lead%20to%20inaccurate%20evaluation%20of%20those%20architectures.To%20reveal%20the%20AV-specific%20model%20insights%2C%20we%20start%20from%20a%20standard%20general-purpose%20encoder%2C%20ConvNeXt%20and%20progressively%20transform%20the%20design.%20We%20adjust%20different%20design%20parameters%20including%20width%20and%20depth%20of%20the%20model%2C%20stage%20compute%20ratio%2C%20attention%20mechanisms%2C%20and%20input%20resolution%2C%20supported%20by%20systematic%20analysis%20to%20each%20modifications.%20This%20customization%20yields%20an%20architecture%20optimized%20for%20AV%20camera%20encoder%20achieving%208.79%25%20mAP%20improvement%20over%20the%20baseline.%20We%20believe%20our%20effort%20could%20become%20a%20sweet%20cookbook%20of%20image%20encoders%20for%20AV%20and%20pave%20the%20way%20to%20the%20next-level%20drive%20system.)
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