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Semantic Segmentation
Semantic Segmentation On Pascal Voc 2012
Semantic Segmentation On Pascal Voc 2012
Métriques
Mean IoU
Résultats
Résultats de performance de divers modèles sur ce benchmark
Columns
Nom du modèle
Mean IoU
Paper Title
Repository
FCN (VGG-16)
62.2%
Fully Convolutional Networks for Semantic Segmentation
Dilated FCN-2s VGG19
69%
Efficient Yet Deep Convolutional Neural Networks for Semantic Segmentation
Light-Weight-RefineNet-50
81.1%
Light-Weight RefineNet for Real-Time Semantic Segmentation
Light-Weight-RefineNet-101
82.0%
Light-Weight RefineNet for Real-Time Semantic Segmentation
DeepLabv3+ (Xception-JFT)
89.0%
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Smooth Network with Channel Attention Block
86.2%
Learning a Discriminative Feature Network for Semantic Segmentation
SANet (pretraining on COCO dataset)
86.1%
Squeeze-and-Attention Networks for Semantic Segmentation
ESPNet
63.01%
ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation
TuSimple
83.1%
Understanding Convolution for Semantic Segmentation
PSPNet
85.4%
Pyramid Scene Parsing Network
ParseNet
69.8%
ParseNet: Looking Wider to See Better
G2
56.7%
Exploiting saliency for object segmentation from image level labels
-
CentraleSupelec Deep G-CRF
80.2%
Fast, Exact and Multi-Scale Inference for Semantic Image Segmentation with Deep Gaussian CRFs
WASPnet-CRF (ours)
79.6%
Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation
EncNet (ResNet-101)
82.9%
Context Encoding for Semantic Segmentation
SSDD
65.5
Self-Supervised Difference Detection for Weakly-Supervised Semantic Segmentation
Dilated Convolutions
67.6%
Multi-Scale Context Aggregation by Dilated Convolutions
CASIA_IVA_SDN
86.6%
Stacked Deconvolutional Network for Semantic Segmentation
-
ESPNetv2
68.0%
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
Deeplab-v2 with Lovasz-Softmax loss
79.00%
The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks
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