Average Recall@IoU:0.5-0.95: 28.0
Papers with Code · CC BY-SA 4.0
Instance Segmentation
Instance Segmentation is a computer vision task that involves identifying and separating individual objects within an image, including detecting the boundaries of each object and assigning a unique label to each object. The goal of instance segmentation is to produce a pixel-wise segmentation map of the image, where each pixel is assigned to a specific object instance.
Image Credit: Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers, CVPR'21
개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.
벤치마크
Papers with Code 아카이브의 데이터셋별 기록 예시입니다. 평가 조건과 지표 방향이 검증되지 않아 순위를 의미하지 않습니다.
데이터셋
108개논문
mask AP: 41.23
mAP@0.5:0.95:0.05: 65.8
Average Recall@IoU:0.5-0.95: 9.4
mAP@0.5:0.95:0.05: 44.06
Dice Coef: 86.15
AP50: 90.7AP75: 82.8
mAP50: 57.5
MOTA: 68.2
라이브러리
Papers with Code 리더보드 row에 연결된 구현 저장소입니다.
하위 태스크
RE
Referring Expression Segmentation
3I
3D Instance Segmentation
UO
Unsupervised Object Segmentation
RI
Real-time Instance Segmentation
IS
Image-level Supervised Instance Segmentation
HI
Human Instance Segmentation
BI
Box-supervised Instance Segmentation
3S
3D Semantic Instance Segmentation
PI
Point-Supervised Instance Segmentation
RI
RGB-D Instance Segmentation
AI
Amodal Instance Segmentation
OI
Open-World Instance Segmentation