Semantic Segmentation On Synpass
Metrics
mIoU
Results
Performance results of various models on this benchmark
Model Name | mIoU | Paper Title | Repository |
|---|---|---|---|
| PVT | 32.68% | Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions | - |
| Trans4PASS+ | 39.16% | Behind Every Domain There is a Shift: Adapting Distortion-aware Vision Transformers for Panoramic Semantic Segmentation | - |
| Fast-SCNN | 21.30% | Fast-SCNN: Fast Semantic Segmentation Network | - |
| DeepLabv3+ | 29.66% | Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation | - |
| Trans4PASS | 38.57% | Bending Reality: Distortion-aware Transformers for Adapting to Panoramic Semantic Segmentation | - |
| SegFomrer | 37.24% | SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers | - |
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