The　majority　of　semantic　segmentation　networks　generally　employ　cross-entropy　as　a　loss　function　and　intersection-over-union　(IoU)　as　the　evaluation　metric　for　network　performance.　Employing　IoU　as　a　loss　function　can　solve　the　mismatch　issue　between　the　loss　function　and　the　evaluation　metric.　We　propose　a　Soft　IoU　training　strategy　based　on　mini-batch　(mini-batch　Soft　IoU).　Our　work　has　two　primary　contributions:　The　first　is　to　extend　the　IoU　loss　function　to　a　multi-class　segmentation　network.　The　second　is　to　collect　various　categories　of　the　training　samples　in　every　mini-batch,　which　will　ensure　that　the　number　of　categories　equals　to　the　batch　size　at　least.　Our　method　breaks　the　randomness　of　the　original　mini-batch　gradient　descent　(GD)　strategy,　advancing　training　samples　in　the　mini-batch　much　more　consistent　with　the　distribution　characteristics　of　the　overall　data.　It　solves　the　instability　of　IoU　loss　function.　In　addition,　the　experimental　results　on　the　PASCAL　VOC2012　dataset　reveal　that　our　method　effectively　improves　the　segmentation　accuracy　of　the　network　and　attains　significant　improvements　beyond　state-of-the-art　IoU　loss　function　methods.