Models　can　be　compared　against　each　other.　Such　comparison　helps　to　find　strengths　and　weaknesses　of　different　approaches　and　gives　additional　possibilities　for　model　validation.　Multi-class　classification　is　a　classification　task　where　each　image　is　assigned　to　one　and　only　one　label.　Confusion　matrix,　Precision,　Recall,　and　F1　Score　are　popular　performance　metrics.　The　common　sense　is　that　the　performance　of　any　architecture　is　dependent　on　the　sizes　of　data　set.　The　goodness　of　the　architecture　of　deep　learning　models　for　different　data　sets　is　critical　issue.　This　paper　implements　Pearson　correlation　coefficient　and　the　multivariate　linear　regression　method　to　assess　the　accuracy　of　deep　learning　architectures　with　five　performance　indicators.　The　five　performance　indicators　are　training　loss　rate,　robustness,　training　time,　number　of　model　parameters　and　computation　complexity.　There　are　five　image　datasets　used　to　test　four　deep　learning　models:　Alexnet,　GoogLeNet,　ResNet,　MobileNet　to　obtain　the　values　of　accuracy　and　other　five　indicators.　The　most　important　contribution　of　the　article　is　to　show　that　the　accuracy　indicator　related　to　training　loss　rate　and　training　time　indicators　are　not　dependent　on　the　selection　of　the　data　group.　According　to　the　definition　of　Measurement　Invariance　(MI),　the　measurement　invariance　is　demonstrated　by　the　linear　regression　analysis　and　inner　product　of　the　unit　normal　vector　of　the　linear　regression　planes.