Semantic Segmentation On Spectralwaste
Metriken
mIoU
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | mIoU | Paper Title | Repository |
|---|---|---|---|
| MiniNet (RGB) | 44.5 | MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation | - |
| SegFormer (HYPER3) | 53.5 | SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers | |
| CMX ( RGB-HYPER3 ) | 56.6 | CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers | |
| MiniNet (HYPER) | 52.8 | MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation | - |
| SegFormer (HYPER) | 54.3 | SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers | |
| CMX (RGB-HYPER) | 58.2 | CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers | |
| SegFormer (RGB) | 48.4 | SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers | |
| MiniNet (HYPER3) | 49.0 | MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation | - |
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