Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/RBE2E

RBE2E

Reported on 10 benchmarks across 2 tasks · 1 paper · 8 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Medical5 results

Semantic SegmentationonSIFT-flow
Mean Accuracy· 2016-07-26
64
SOTA
Region-based semantic segmentation with end-to-end training arXiv:1607.07671
Semantic SegmentationonSIFT-flow
Pixel Accuracy· 2016-07-26
84.3
SOTA
Region-based semantic segmentation with end-to-end training arXiv:1607.07671
Semantic SegmentationonPASCAL Context
Mean Accuracy· 2016-07-26
49.9
SOTA
Region-based semantic segmentation with end-to-end training arXiv:1607.07671
Semantic SegmentationonPASCAL Context
Pixel Accuracy· 2016-07-26
62.4
SOTA
Region-based semantic segmentation with end-to-end training arXiv:1607.07671
Semantic SegmentationonPASCAL Context
mIoU· 2016-07-26
32.5
best: 71.1 (VPNeXt)
Region-based semantic segmentation with end-to-end training arXiv:1607.07671

Audio5 results

10-shot image generationonSIFT-flow
Mean Accuracy· 2016-07-26
64
SOTA
Region-based semantic segmentation with end-to-end training arXiv:1607.07671
10-shot image generationonSIFT-flow
Pixel Accuracy· 2016-07-26
84.3
SOTA
Region-based semantic segmentation with end-to-end training arXiv:1607.07671
10-shot image generationonPASCAL Context
Mean Accuracy· 2016-07-26
49.9
SOTA
Region-based semantic segmentation with end-to-end training arXiv:1607.07671
10-shot image generationonPASCAL Context
Pixel Accuracy· 2016-07-26
62.4
SOTA
Region-based semantic segmentation with end-to-end training arXiv:1607.07671
10-shot image generationonPASCAL Context
mIoU· 2016-07-26
32.5
best: 71.1 (VPNeXt)
Region-based semantic segmentation with end-to-end training arXiv:1607.07671