Rock　image　classification　represents　a　challenging　fine-grained　image　classification　task　characterized　by　subtle　differences　among　closely　related　rock　categories.　Current　contrastive　learning　methods　prevalently　utilized　in　fine-grained　image　classification　restrict　the　model＇s　capacity　to　discern　critical　features　contrastively　from　image　pairs,　and　are　typically　too　large　for　deployment　on　mobile　devices　used　for　in　situ　rock　identification.　In　this　work,　we　introduce　an　innovative　and　compact　model　generation　framework　anchored　by　the　design　of　a　Feature　Positioning　Comparison　Network　(FPCN).　The　FPCN　facilitates　interaction　between　feature　vectors　from　localized　regions　within　image　pairs,　capturing　both　shared　and　distinctive　features.　Further,　it　accommodates　the　variable　scales　of　objects　depicted　in　images,　which　correspond　to　differing　quantities　of　inherent　object　information,　directing　the　network＇s　attention　to　additional　contextual　details　based　on　object　size　variability.　Leveraging　knowledge　distillation,　the　architecture　is　streamlined,　with　a　focus　on　nuanced　information　at　activation　boundaries　to　master　the　precise　fine-grained　decision　boundaries,　thereby　enhancing　the　small　model＇s　accuracy.　Empirical　evidence　demonstrates　that　our　proposed　method　based　on　FPCN　improves　the　classification　accuracy　mobile　lightweight　models　by　nearly　2%　while　maintaining　the　same　time　and　space　consumption.