In　industrial　manufacturing,　defect　detection　is　essential.　Since　the　2020＇s　ViT　(vision　transformer)　hit　the　scene,　ViT　has　been　increasingly　used　for　defect　detection　tasks　in　the　vision　domain.　The　advantage　of　ViT　over　convolutional　neural　networks　(CNNs)　is　its　ability　to　capture　global　remote　dependencies　to　learn　better　features.　In　addition　to　this,　contrast　learning　based　on　self-supervised　methods　has　been　well　used　in　defect　detection　tasks.　In　this　study,　we　suggest　a　strategy　for　detecting　fabric　defects　that　combines　transformer　and　contrast　learning.　First,　we　propose　a　new　backbone　network　CViT　(convolutional　vision　transformer),　which　is　improved　relative　to　ViT　by　adding　a　convolutional　attention　module　to　the　ordinary　transformer　block　structure　while　using　depthwise　separable　convolution　instead　of　linear　projection　to　obtain　q,　k,　and　v　for　attention　computation.　Second,　to　compensate　for　the　potential　instability　of　CViT,　instead　of　the　16　×　16　big　convolutions　used　in　the　ViT,　we　use　several　stacked　3　×　3　tiny　convolutions　to　divide　each　enhanced　sample　into　a　series　of　patches.　Third,　we　incorporate　conditional　position　encoding(CPE)　and　explore　the　impact　of　different　position　encodings　on　model　performance.　Finally,　the　effectiveness　of　our　model　is　demonstrated　on　three　classical　public　datasets　for　fabric　fault　detection.　©　2024　SPIE.　All　rights　reserved.