Anomaly　detection　is　represented　as　an　unsupervised　learning　to　identify　deviated　images　from　normal　images.　In　general,　there　are　two　main　challenges　of　anomaly　detection　tasks,　i.e.,　the　class　imbalance　and　the　unexpectedness　of　anomalies.　In　this　paper,　we　propose　a　multiresolution　feature　guidance　method　based　on　Transformer　named　GTrans　for　unsupervised　anomaly　detection　and　localization.　In　GTrans,　an　Anomaly　Guided　Network　(AGN)　pre-trained　on　ImageNet　is　developed　to　provide　surrogate　labels　for　features　and　tokens.　Under　the　tacit　knowledge　guidance　of　the　AGN,　the　anomaly　detection　network　named　Trans　utilizes　Transformer　to　effectively　establish　a　relationship　between　features　with　multiresolution,　enhancing　the　ability　of　the　Trans　in　fitting　the　normal　data　manifold.　Due　to　the　strong　generalization　ability　of　AGN,　GTrans　locates　anomalies　by　comparing　the　differences　in　spatial　distance　and　direction　of　multi-scale　features　extracted　from　the　AGN　and　the　Trans.　Our　experiments　demonstrate　that　the　proposed　GTrans　achieves　state-of-the-art　performance　in　both　detection　and　localization　on　the　MVTec　AD　dataset.　GTrans　achieves　image-level　and　pixel-level　anomaly　detection　AUROC　scores　of　99.0%　and　97.9%　on　the　MVTec　AD　dataset,　respectively.