HyperAI

Semi Supervised Semantic Segmentation On 4

Metriken

Validation mIoU

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Vergleichstabelle
ModellnameValidation mIoU
the-gist-and-rist-of-iterative-self-training70.76%
dense-fixmatch-a-simple-semi-supervised65.82%
confidence-weighted-boundary-aware-learning75.81%
classmix-segmentation-based-data-augmentation71.00%
semi-supervised-semantic-segmentation-with-271.6%
semi-supervised-semantic-segmentation-using-279.01%
perturbed-and-strict-mean-teachers-for-semi75.70%
guidedmix-net-learning-to-improve-pseudo76.4%
semi-supervised-semantic-segmentation-with-680.71%
semi-supervised-semantic-segmentation-with71.4%
conservative-progressive-collaborative73.74%
conservative-progressive-collaborative76.4%
semi-supervised-semantic-segmentation-with-575.10%
semi-supervised-semantic-segmentation-via-379.67%
consistency-regularization-and-cutmix-for72.45%
dense-fixmatch-a-simple-semi-supervised62.49%
bootstrapping-semantic-segmentation-with71.00%
semi-supervised-semantic-segmentation-with-376.44%
semi-supervised-semantic-segmentation-with70.4%
semi-supervised-semantic-segmentation-via-277.57%
allspark-reborn-labeled-features-from82.04%
semi-supervised-semantic-segmentation-via72.70%
semi-supervised-semantic-segmentation-with67.3%
n-cps-generalising-cross-pseudo-supervision77.99%
revisiting-weak-to-strong-consistency-in-semi81.92%
learning-pseudo-labels-for-semi-and-weakly75.52%
adversarial-learning-for-semi-supervised64.3%
consistency-regularization-and-cutmix-for67.6%
semi-supervised-semantic-segmentation-with-573.2%
revisiting-and-maximizing-temporal-knowledge81.9
perturbed-and-strict-mean-teachers-for-semi78.20%
guidedmix-net-learning-to-improve-pseudo73.4%
corrmatch-label-propagation-via-correlation81.9%
n-cps-generalising-cross-pseudo-supervision74.21%
switching-temporary-teachers-for-semi81.19