Papers with Code 아카이브

Real-Time Semantic Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 8
보존된 평가 기록: 108

Semantic Segmentation is a computer vision task that involves assigning a semantic label to each pixel in an image. In Real-Time Semantic Segmentation, the goal is to perform this labeling quickly and accurately in real-time, allowing for the segmentation results to be used for tasks such as object recognition, scene understanding, and autonomous navigation.

<span style="color:grey; opacity: 0.6">( Image credit: TorchSeg )</span>

평가 결과

데이터셋과 평가 조건을 선택해 성능·논문·코드를 비교하세요. 보존된 Papers with Code 기록으로, 최신 순위는 아닙니다.

Cityscapes test

원본 태스크: 10-shot image generation / Semantic Segmentation / Real-Time Semantic Segmentation

평가 기록 39건

CamVid

원본 태스크: 10-shot image generation / Semantic Segmentation / Real-Time Semantic Segmentation

평가 기록 29건

Cityscapes val

원본 태스크: 10-shot image generation / Semantic Segmentation / Real-Time Semantic Segmentation

평가 기록 24건

NYU Depth v2

원본 태스크: 10-shot image generation / Semantic Segmentation / Real-Time Semantic Segmentation

평가 기록 10건

Cityscapes

원본 태스크: 10-shot image generation / Semantic Segmentation / Real-Time Semantic Segmentation

평가 기록 2건

COCO-Stuff

원본 태스크: 10-shot image generation / Semantic Segmentation / Real-Time Semantic Segmentation

평가 기록 2건

FLAME

원본 태스크: 10-shot image generation / Semantic Segmentation / Real-Time Semantic Segmentation

평가 기록 1건

HelixNet

원본 태스크: 10-shot image generation / Semantic Segmentation / Real-Time Semantic Segmentation

평가 기록 1건

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.