Efficient　and　convenient　rock　image　classification　methods　are　important　for　geological　research.　They　help　in　identifying　and　categorizing　rocks　based　on　their　physical　and　chemical　properties,　which　can　provide　insights　into　their　geological　history,　origin,　and　potential　uses　in　various　applications.　The　classification　and　identification　of　rocks　often　rely　on　experienced　and　knowledgeable　professionals　and　are　less　efficient.　Fine-grained　rock　image　classification　is　a　challenging　task　because　of　the　inherent　subtle　differences　between　highly　confusing　categories,　which　require　a　large　number　of　data　samples　and　computational　resources,　resulting　in　low　recognition　accuracy,　and　are　difficult　to　apply　in　mobile　scenarios,　requiring　the　design　of　a　high-performance　image　processing　classification　architecture.　In　this　paper　we　design　a　knowledge　distillation　and　high-accuracy　feature　localization　comparison　network　(FPCN)-based　learning　architecture　for　generating　small　high-performance　rock　image　classification　models.　Specifically,　for　a　pair　of　images,　we　interact　with　the　feature　vectors　generated　from　the　localized　feature　maps　to　capture　common　and　unique　features,　let　the　network　focus　on　more　complementary　information　according　to　the　different　scales　of　the　objects,　and　then　the　important　features　of　the　images　learned　in　this　way　are　made　available　for　the　micro-model　to　learn　the　critical　information　for　discrimination　via　model　distillation.　The　proposed　method　improves　the　accuracy　of　the　micro-model　by　3%.