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/ACDNet

ACDNet

Reported on 8 benchmarks across 4 tasks · 2 papers · 2 SOTA

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

Methodology4 results

ClassificationonESC-50
Accuracy (5-fold)· 2021-03-05
87.1
best: 99.1 (OmniVec2)
SOTA
Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices arXiv:2103.03483
3DonStanford2D3D Panoramic
RMSE· 2021-12-29
0.341
best: 0.2619 (HiMODE)
ACDNet: Adaptively Combined Dilated Convolution for Monocular Panorama Depth Estimation arXiv:2112.14440
3DonStanford2D3D Panoramic
absolute relative error· 2021-12-29
0.0984
best: 0.0405 (PanoFormer)
ACDNet: Adaptively Combined Dilated Convolution for Monocular Panorama Depth Estimation arXiv:2112.14440
ClassificationonESC-50
Top-1 Accuracy· 2021-03-05
87.1
best: 99.1 (OmniVec2)
Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices arXiv:2103.03483

Audio2 results

Audio ClassificationonESC-50
Accuracy (5-fold)· 2021-03-05
87.1
best: 99.1 (OmniVec2)
SOTA
Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices arXiv:2103.03483
Audio ClassificationonESC-50
Top-1 Accuracy· 2021-03-05
87.1
best: 99.1 (OmniVec2)
Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices arXiv:2103.03483

Computer Vision2 results

Depth EstimationonStanford2D3D Panoramic
RMSE· 2021-12-29
0.341
best: 0.2619 (HiMODE)
ACDNet: Adaptively Combined Dilated Convolution for Monocular Panorama Depth Estimation arXiv:2112.14440
Depth EstimationonStanford2D3D Panoramic
absolute relative error· 2021-12-29
0.0984
best: 0.0405 (PanoFormer)
ACDNet: Adaptively Combined Dilated Convolution for Monocular Panorama Depth Estimation arXiv:2112.14440