Semantic Segmentation On Coco Stuff Test
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
比较表格
| 模型名称 | mIoU |
|---|---|
| caa-channelized-axial-attention-for-semantic | 41.2% |
| efficient-self-ensemble-framework-for-1 | 50.1% |
| scene-segmentation-with-dual-relation-aware | 41.2% |
| context-contrasted-feature-and-gated-multi | 35.7% |
| object-contextual-representations-for | 39.5% |
| dag-recurrent-neural-networks-for-scene | 31.2% |
| fully-convolutional-networks-for-semantic-1 | 22.7% |
| object-contextual-representations-for | 40.5% |
| vpnext-rethinking-dense-decoding-for-plain | 53.7 |
| eva-exploring-the-limits-of-masked-visual | 53.4% |
| segvit-semantic-segmentation-with-plain | 50.3% |
| expectation-maximization-attention-networks | 39.9% |
| representation-separation-for-semantic | 52.0% |
| dual-attention-network-for-scene-segmentation | 39.7% |
| semantic-correlation-promoted-shape-variant-1 | 39.6% |
| representation-separation-for-semantic | 52.6% |
| refinenet-multi-path-refinement-networks-for | 33.6% |
| caa-channelized-axial-attention-for-semantic | 45.4% |
| object-contextual-representations-for | 45.2% |
| asymmetric-non-local-neural-networks-for | 37.2% |