[{"data":1,"prerenderedAt":156},["ShallowReactive",2],{"glossary-en-instance-segmentation":3},{"id":4,"title":5,"body":6,"description":142,"extension":143,"meta":144,"navigation":151,"path":152,"seo":153,"stem":154,"__hash__":155},"en_glossary\u002Fen\u002Fglossary\u002Finstance-segmentation.md","What is Instance Segmentation?",{"type":7,"value":8,"toc":132},"minimark",[9,13,27,32,87,91,98,102,124],[10,11,5],"h2",{"id":12},"what-is-instance-segmentation",[14,15,16,20,21,26],"p",{},[17,18,19],"strong",{},"Instance segmentation"," is the ",[22,23,25],"a",{"href":24},"\u002Fen\u002Fglossary\u002Fcomputer-vision","computer vision"," task of finding each object in an image separately and extracting each one's outline at pixel level.",[28,29,31],"h3",{"id":30},"three-approaches-compared","Three Approaches Compared",[33,34,35,51],"table",{},[36,37,38],"thead",{},[39,40,41,45,48],"tr",{},[42,43,44],"th",{},"Task",[42,46,47],{},"Output",[42,49,50],{},"Weakness",[52,53,54,66,77],"tbody",{},[39,55,56,60,63],{},[57,58,59],"td",{},"Object detection",[57,61,62],{},"One box per object",[57,64,65],{},"The box includes pixels outside the object",[39,67,68,71,74],{},[57,69,70],{},"Semantic segmentation",[57,72,73],{},"One class per pixel",[57,75,76],{},"Cannot separate two touching objects",[39,78,79,81,84],{},[57,80,19],{},[57,82,83],{},"A separate pixel mask per object",[57,85,86],{},"The most expensive to label",[28,88,90],{"id":89},"when-do-you-need-it","When Do You Need It?",[14,92,93,94,97],{},"When you need to count objects ",[17,95,96],{},"and"," measure inside each one. For example, finding each tree's crown in a drone image and computing NDVI inside it. With boxes, the soil and grass between trees would spoil the measurement.",[28,99,101],{"id":100},"common-models","Common Models",[103,104,105,112,118],"ul",{},[106,107,108,111],"li",{},[17,109,110],{},"YOLOv8-seg \u002F YOLO11-seg",": fast, single-stage; close to real time in the field.",[106,113,114,117],{},[17,115,116],{},"Mask R-CNN",": two-stage, slower, but strong on small objects.",[106,119,120,123],{},[17,121,122],{},"SAM (Segment Anything)",": produces masks zero-shot; often used to speed up labelling.",[14,125,126,127,131],{},"For a worked example see ",[22,128,130],{"href":129},"\u002Fen\u002Fblog\u002Fwalnut-crown-detection-training-yolov8-seg","Walnut Crown Detection",".",{"title":133,"searchDepth":134,"depth":134,"links":135},"",2,[136],{"id":12,"depth":134,"text":5,"children":137},[138,140,141],{"id":30,"depth":139,"text":31},3,{"id":89,"depth":139,"text":90},{"id":100,"depth":139,"text":101},"The difference between object detection, semantic segmentation and instance segmentation, and which one to pick for which problem.","md",{"tags":145},[146,147,148,149,150],"Computer Vision","Deep Learning","Segmentation","YOLO","Industrial AI",true,"\u002Fen\u002Fglossary\u002Finstance-segmentation",{"title":5,"description":142},"en\u002Fglossary\u002Finstance-segmentation","MjBazeAFlo96g9LzrFG-0BofHSVeRBse1Mrfrc8LHo8",1793875325263]