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/AMD

AMD

Reported on 22 benchmarks across 5 tasks · 2 papers · 4 SOTA

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

Medical12 results

Medical Image SegmentationonSUN-SEG-Easy (Unseen)
Dice· 2021-11-11
0.266
best: 0.9 (YOLO-SAM 2)
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos arXiv:2111.06394
Medical Image SegmentationonSUN-SEG-Easy (Unseen)
S measure· 2021-11-11
0.474
best: 0.9 (YOLO-SAM 2)
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos arXiv:2111.06394
Medical Image SegmentationonSUN-SEG-Easy (Unseen)
Sensitivity· 2021-11-11
0.222
best: 83.7 (YOLO-SAM 2)
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos arXiv:2111.06394
Medical Image SegmentationonSUN-SEG-Easy (Unseen)
mean E-measure· 2021-11-11
0.533
best: 93.8 (YOLO-SAM 2)
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos arXiv:2111.06394
Medical Image SegmentationonSUN-SEG-Easy (Unseen)
mean F-measure· 2021-11-11
0.146
best: 93.8 (YOLO-SAM 2)
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos arXiv:2111.06394
Medical Image SegmentationonSUN-SEG-Easy (Unseen)
weighted F-measure· 2021-11-11
0.133
best: 0.794 (SALI)
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos arXiv:2111.06394
Medical Image SegmentationonSUN-SEG-Hard (Unseen)
Dice· 2021-11-11
0.252
best: 0.902 (YOLO-SAM 2)
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos arXiv:2111.06394
Medical Image SegmentationonSUN-SEG-Hard (Unseen)
S-Measure· 2021-11-11
0.472
best: 0.894 (YOLO-SAM 2)
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos arXiv:2111.06394
Medical Image SegmentationonSUN-SEG-Hard (Unseen)
Sensitivity· 2021-11-11
0.213
best: 0.852 (YOLO-SAM 2)
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos arXiv:2111.06394
Medical Image SegmentationonSUN-SEG-Hard (Unseen)
mean E-measure· 2021-11-11
0.527
best: 0.941 (YOLO-SAM 2)
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos arXiv:2111.06394
Medical Image SegmentationonSUN-SEG-Hard (Unseen)
mean F-measure· 2021-11-11
0.141
best: 0.932 (YOLO-SAM 2)
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos arXiv:2111.06394
Medical Image SegmentationonSUN-SEG-Hard (Unseen)
weighted F-measure· 2021-11-11
0.128
best: 0.79 (SALI)
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos arXiv:2111.06394

Computer Vision6 results

Instance SegmentationonSegTrack-v2
mIoU· 2021-11-11
57
best: 79.6 (RCF (with post-processing))
SOTA
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos arXiv:2111.06394
Instance SegmentationonFBMS-59
mIoU· 2021-11-11
47.5
best: 72.4 (RCF (with post-processing))
SOTA
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos arXiv:2111.06394
Unsupervised Object SegmentationonSegTrack-v2
mIoU· 2021-11-11
57
best: 79.6 (RCF (with post-processing))
SOTA
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos arXiv:2111.06394
Unsupervised Object SegmentationonFBMS-59
mIoU· 2021-11-11
47.5
best: 72.4 (RCF (with post-processing))
SOTA
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos arXiv:2111.06394
Instance SegmentationonDAVIS 2016
J score· uses extra data· 2021-11-11
57.8
best: 83 (RCF (with Post-Processing))
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos arXiv:2111.06394
Unsupervised Object SegmentationonDAVIS 2016
J score· uses extra data· 2021-11-11
57.8
best: 83 (RCF (with Post-Processing))
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos arXiv:2111.06394

Time Series4 results

Time Series ForecastingonETTh1 (336) Multivariate
MAE· 2024-06-06
0.427
best: 0.2158 (DeformTime)
Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting arXiv:2406.03751
Time Series ForecastingonETTh1 (336) Multivariate
MSE· 2024-06-06
0.418
best: 0.374 (D-PAD)
Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting arXiv:2406.03751
Time Series AnalysisonETTh1 (336) Multivariate
MAE· 2024-06-06
0.427
best: 0.2158 (DeformTime)
Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting arXiv:2406.03751
Time Series AnalysisonETTh1 (336) Multivariate
MSE· 2024-06-06
0.418
best: 0.374 (D-PAD)
Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting arXiv:2406.03751