Semi Supervised Semantic Segmentation On 15
Metrics
Validation mIoU
Results
Performance results of various models on this benchmark
Comparison Table
| Model Name | Validation mIoU |
|---|---|
| conservative-progressive-collaborative | 77.67% |
| semi-supervised-semantic-segmentation-via-3 | 81.11 |
| dense-fixmatch-a-simple-semi-supervised | 74.73% |
| n-cps-generalising-cross-pseudo-supervision | 77.07% |
| conservative-progressive-collaborative | 75.3% |
| semi-supervised-semantic-segmentation-with-6 | 80.91% |
| semi-supervised-semantic-segmentation-using-2 | 80.5% |
| guidedmix-net-learning-to-improve-pseudo | 76.5% |
| learning-pseudo-labels-for-semi-and-weakly | 77.26% |
| perturbed-and-strict-mean-teachers-for-semi | 79.76% |
| n-cps-generalising-cross-pseudo-supervision | 80.26% |
| dense-fixmatch-a-simple-semi-supervised | 71.69% |
| guidedmix-net-learning-to-improve-pseudo | 78.2% |
| semi-supervised-semantic-segmentation-via-2 | 80.29% |