Intelligent　reflecting　surfaces　(IRSs)　have　been　proposed　in　recent　years　as　a　promising　technology　to　enhance　the　quality　of　transmissions　in　high-frequency　spectrum.　Currently,　the　research　on　the　performance　of　large　networks　with　IRSs　is　still　in　its　infancy.　Different　from　the　commonly-used　stochastic　geometry　model　for　the　study　of　traditional　networks,　where　only　transmitters　and　receivers　are　modeled　as　point　processes,　in　an　IRS　network,　the　blockages　and　reflectors　also　need　to　be　accounted　for.　In　this　paper,　we　study　a　bipolar　network　with　a　line　segment　object　model,　and　derive　the　probability　that　an　IRS　can　successfully　reflect　a　signal　from　a　transmitter　to　a　receiver,　as　well　as　the　distribution　of　the　distance　traveled　by　the　reflected　signal.　With　these　analytic　results,　the　signal　to　interference　ratio　(SIR)　and　the　achievable　rate　are　obtained　in　closed-form　expressions.　From　the　analysis,　we　can　observe　that　IRSs　have　a　great　potential　to　enhance　the　network　performance,　as　they　are　able　to　boost　the　signal　power,　while　preventing　the　inter-cell　interference　from　rising　rapidly.　More　importantly,　we　find　that　even　with　a　limited　number　of　IRSs,　the　network　can　still　achieve　a　higher　achievable　rate　than　a　conventional　one　without　IRSs.