The paper measures SAM-B and SAM-H on COCO val2017 for annotation-free instance segmentation. SAM achieves high average recall (AR100 of 36.9 for SAM-B, 44.5 for SAM-H) but very low average precision (AP of 1.4 and 0.9 respectively). The authors attribute this to SAM's edge-oriented approach: it segments all prominent edges including interior edges within objects (e.g., between an elephant's head and ear), and tends to assign higher confidence to smoother planes and smaller objects rather than to actual object instances. This makes it impossible to determine the confidence that a particular mask represents an object.