HyperAI超神经
首页资讯最新论文教程数据集百科SOTALLM 模型天梯GPU 天梯顶会
全站搜索
关于
HyperAI超神经

Real Time Semantic Segmentation On Cityscapes 1

评估指标

mIoU

评测结果

各个模型在此基准测试上的表现结果

模型名称
mIoU
Paper TitleRepository
PP-LiteSeg-T173.1PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model
SegNext-T-Seg10079.8%SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation
DSNet80.4%DSNet: A Novel Way to Use Atrous Convolutions in Semantic Segmentation
RTFormer-S76.3%RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer
PIDNet-M79.9%PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers
DDRNet23-slim77.4Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes
DDRNet2379.4Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes
PIDNet-S78.8%PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers
CSFNet-174.73CSFNet: A Cosine Similarity Fusion Network for Real-Time RGB-X Semantic Segmentation of Driving Scenes-
RTFormer-B79.3%RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer
LiteSeg-MobileNet67.8%LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation
FasterSeg73.1FasterSeg: Searching for Faster Real-time Semantic Segmentation
PIDNet-L80.9%PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers
BiseNetV2-L75.8%BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation
CSFNet-276.36CSFNet: A Cosine Similarity Fusion Network for Real-Time RGB-X Semantic Segmentation of Driving Scenes-
PP-LiteSeg-B278.2PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model
SwiftNetRN-1875.5%In defence of metric learning for speaker recognition
BiseNetV273.5%BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation
PP-LiteSeg-T276PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model
Mobile-Seed78.4%Mobile-Seed: Joint Semantic Segmentation and Boundary Detection for Mobile Robots
0 of 24 row(s) selected.