The　current　mainstream　studies　on　Scene　Graph　Generation　(SGG)　devote　to　the　long-tailed　predicate　distribution　problem　to　generate　unbiased　scene　graph.　The　long-tailed　predicate　distribution　exists　in　VG　dataset　and　is　more　severe　during　the　SGG　network　training　process.　Most　existing　de-biasing　methods　solve　the　problem　by　applying　re-　sampling　or　re-　weighting　in　a　mini-batch,　with　the　main　idea　being　to　provide　unbiased　attention　to　different　predicate　categories　based　on　prior　predicate　distributions.　During　the　training　process　of　SGG　models,　existing　training　mode　samples　several　images　into　a　mini-batch　to　obtain　training　data,　thus　providing　sparse　and　scattered　predicate　instances　for　training.　However,　sampling　predicate　instances　from　a　limited　set　of　predicate　samples　in　terms　of　quantity　and　category　poses　difficulties　in　training　unbiased　SGG　models.　In　order　to　provide　a　wider　range　for　sampling　predicate　instances,　this　paper　reorganizes　the　images　in　VG　training　set　with　a　new　form,　i.e.　object-pairs,　and　constructs　VG-OP　(VG　Object-Pair)　training　set　to　save　object-pairs.　Meanwhile,　this　paper　introduces　a　new　SGG　network　training　mode,　which　can　realize　unbiased　SGG　without　re-　sampling　or　re-　weighting.　In　particular,　a　Predicate-balanced　Sampling　Network　(PS-Net)　is　designed　to　validate　the　new　training　mode.　Extensive　experiments　on　VG　test　set　demonstrate　that　our　method　achieves　competitive　or　state-of-the-art　unbiased　SGG　performance.