Instance Segmentation
Instance segmentation is a task in the field of computer vision that aims to identify individual objects in an image and separate each object's boundary while assigning a unique label to each object. Its goal is to generate a pixel-level segmentation map, ensuring that each pixel in the image is accurately attributed to a specific object instance, thereby achieving precise localization and differentiation of multiple objects in complex scenes. This technology has significant value in applications such as autonomous driving, medical image analysis, and robotic vision.
ADE20K val
OneFormer (DiNAT-L, single-scale)
ARMBench
RISE (VIT-B)
BDD100K val
Mask Transfiner
Box-IS
Cityscapes test
PolyTransform
Cityscapes val
OpenSeeD( SwinL, single-scale)
COCO 2017 val
SparK (ConvNeXt V1-B Mask R-CNN)
COCO
ColorMAE-Green-ViTB-1600
COCO minival
Co-DETR
coco minval
R3-CNN (ResNet-50-FPN, GC-Net)
COCO-N Medium
Mask R-CNN ResNet-50 FPN
COCO test-dev
Co-DETR
COCO val (panoptic labels)
COCO val2017
MogaNet-S (256x192)
iSAID
iShape
KINS
BCNet
LDD
Leaf Segmentation Challenge
LeafMask
LVIS v1.0 test-dev
LVIS v1.0 val
Co-DETR (single-scale)
nuScenes
TraDeS
NYU Depth v2
SGPN-CNN
NYUDv2-IS
Occluded COCO
OoDIS
PartNet
Separated COCO
Swin-B + Cascade Mask R-CNN (tri-layer modelling)
SUN-RGBD-IS
IAM + SOLQ
TBBR
TexBiG 2022 test
VSR (Vison, Semantics and Relation Model)
TexBiG 2023 test
UAVBillboards
YOLOv8-X
UFBA-425
BB-UNet
UIIS
WaterMask RCNN