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Models/UNet

UNet

Reported on 208 benchmarks across 15 tasks · 4 papers · 189 SOTA

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

Methodology112 results

Electrocardiography (ECG)onLUDB
F1 score· 2018-08-30
98.51
SOTA
LUDB: a new open-access validation tool for electrocardiogram delineation algorithms arXiv:1809.03393
Medical waveform analysisonLUDB
F1 score· 2018-08-30
98.51
SOTA
LUDB: a new open-access validation tool for electrocardiogram delineation algorithms arXiv:1809.03393
3DonDIS-TE4
E-measure· 2015-05-18
0.821
best: 0.944 (MVANet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-TE4
HCE· 2015-05-18
3218
best: 3999 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-TE4
MAE· 2015-05-18
0.102
best: 0.037 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-TE4
max F-Measure· 2015-05-18
0.759
best: 0.912 (MVANet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-TE4
weighted F-measure· 2015-05-18
0.659
best: 0.867 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-VD
E-measure· 2015-05-18
0.785
best: 0.958 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-VD
HCE· 2015-05-18
1337
best: 1660 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-VD
MAE· 2015-05-18
0.113
best: 0.027 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-VD
S-Measure· 2015-05-18
0.745
best: 0.917 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-VD
max F-Measure· 2015-05-18
0.692
best: 0.923 (BEN_Base)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-VD
weighted F-measure· 2015-05-18
0.586
best: 0.896 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-TE2
HCE· 2015-05-18
474
best: 621 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-TE2
MAE· 2015-05-18
0.107
best: 0.028 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-TE2
S-Measure· 2015-05-18
0.755
best: 0.924 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-TE2
max F-Measure· 2015-05-18
0.703
best: 0.921 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-TE2
weighted F-measure· 2015-05-18
0.597
best: 0.885 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-TE1
E-measure· 2015-05-18
0.75
best: 0.927 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-TE1
HCE· 2015-05-18
233
best: 288 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-TE1
MAE· 2015-05-18
0.106
best: 0.031 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-TE1
S-Measure· 2015-05-18
0.716
best: 0.899 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-TE1
max F-Measure· 2015-05-18
0.625
best: 0.89 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-TE1
weighted F-measure· 2015-05-18
0.514
best: 0.846 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-TE3
HCE· 2015-05-18
883
best: 1146 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-TE3
MAE· 2015-05-18
0.098
best: 0.027 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-TE3
max F-Measure· 2015-05-18
0.748
best: 0.936 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonDIS-TE3
weighted F-measure· 2015-05-18
0.644
best: 0.9 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
3DonSTARE
AUC· 2015-05-18
0.78
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-TE4
E-measure· 2015-05-18
0.821
best: 0.944 (MVANet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-TE4
HCE· 2015-05-18
3218
best: 3999 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-TE4
MAE· 2015-05-18
0.102
best: 0.037 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-TE4
max F-Measure· 2015-05-18
0.759
best: 0.912 (MVANet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-TE4
weighted F-measure· 2015-05-18
0.659
best: 0.867 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-VD
E-measure· 2015-05-18
0.785
best: 0.958 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-VD
HCE· 2015-05-18
1337
best: 1660 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-VD
MAE· 2015-05-18
0.113
best: 0.027 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-VD
S-Measure· 2015-05-18
0.745
best: 0.917 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-VD
max F-Measure· 2015-05-18
0.692
best: 0.923 (BEN_Base)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-VD
weighted F-measure· 2015-05-18
0.586
best: 0.896 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-TE2
HCE· 2015-05-18
474
best: 621 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-TE2
MAE· 2015-05-18
0.107
best: 0.028 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-TE2
S-Measure· 2015-05-18
0.755
best: 0.924 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-TE2
max F-Measure· 2015-05-18
0.703
best: 0.921 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-TE2
weighted F-measure· 2015-05-18
0.597
best: 0.885 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-TE1
E-measure· 2015-05-18
0.75
best: 0.927 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-TE1
HCE· 2015-05-18
233
best: 288 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-TE1
MAE· 2015-05-18
0.106
best: 0.031 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-TE1
S-Measure· 2015-05-18
0.716
best: 0.899 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-TE1
max F-Measure· 2015-05-18
0.625
best: 0.89 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-TE1
weighted F-measure· 2015-05-18
0.514
best: 0.846 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-TE3
HCE· 2015-05-18
883
best: 1146 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-TE3
MAE· 2015-05-18
0.098
best: 0.027 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-TE3
max F-Measure· 2015-05-18
0.748
best: 0.936 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonDIS-TE3
weighted F-measure· 2015-05-18
0.644
best: 0.9 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D ClassificationonSTARE
AUC· 2015-05-18
0.78
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-TE4
E-measure· 2015-05-18
0.821
best: 0.944 (MVANet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-TE4
HCE· 2015-05-18
3218
best: 3999 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-TE4
MAE· 2015-05-18
0.102
best: 0.037 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-TE4
max F-Measure· 2015-05-18
0.759
best: 0.912 (MVANet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-TE4
weighted F-measure· 2015-05-18
0.659
best: 0.867 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-VD
E-measure· 2015-05-18
0.785
best: 0.958 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-VD
HCE· 2015-05-18
1337
best: 1660 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-VD
MAE· 2015-05-18
0.113
best: 0.027 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-VD
S-Measure· 2015-05-18
0.745
best: 0.917 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-VD
max F-Measure· 2015-05-18
0.692
best: 0.923 (BEN_Base)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-VD
weighted F-measure· 2015-05-18
0.586
best: 0.896 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-TE2
HCE· 2015-05-18
474
best: 621 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-TE2
MAE· 2015-05-18
0.107
best: 0.028 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-TE2
S-Measure· 2015-05-18
0.755
best: 0.924 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-TE2
max F-Measure· 2015-05-18
0.703
best: 0.921 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-TE2
weighted F-measure· 2015-05-18
0.597
best: 0.885 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-TE1
E-measure· 2015-05-18
0.75
best: 0.927 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-TE1
HCE· 2015-05-18
233
best: 288 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-TE1
MAE· 2015-05-18
0.106
best: 0.031 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-TE1
S-Measure· 2015-05-18
0.716
best: 0.899 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-TE1
max F-Measure· 2015-05-18
0.625
best: 0.89 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-TE1
weighted F-measure· 2015-05-18
0.514
best: 0.846 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-TE3
HCE· 2015-05-18
883
best: 1146 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-TE3
MAE· 2015-05-18
0.098
best: 0.027 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-TE3
max F-Measure· 2015-05-18
0.748
best: 0.936 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononDIS-TE3
weighted F-measure· 2015-05-18
0.644
best: 0.9 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononSTARE
AUC· 2015-05-18
0.78
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononPST900
mIoU· 2015-05-18
52.8
best: 89.8 (SHIFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Object DetectiononMFN Dataset
mIOU· 2015-05-18
45.1
best: 62.7 (RoadFormer+ (ConvNeXt-L))
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-TE4
E-measure· 2015-05-18
0.821
best: 0.944 (MVANet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-TE4
HCE· 2015-05-18
3218
best: 3999 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-TE4
MAE· 2015-05-18
0.102
best: 0.037 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-TE4
max F-Measure· 2015-05-18
0.759
best: 0.912 (MVANet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-TE4
weighted F-measure· 2015-05-18
0.659
best: 0.867 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-VD
E-measure· 2015-05-18
0.785
best: 0.958 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-VD
HCE· 2015-05-18
1337
best: 1660 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-VD
MAE· 2015-05-18
0.113
best: 0.027 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-VD
S-Measure· 2015-05-18
0.745
best: 0.917 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-VD
max F-Measure· 2015-05-18
0.692
best: 0.923 (BEN_Base)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-VD
weighted F-measure· 2015-05-18
0.586
best: 0.896 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-TE2
HCE· 2015-05-18
474
best: 621 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-TE2
MAE· 2015-05-18
0.107
best: 0.028 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-TE2
S-Measure· 2015-05-18
0.755
best: 0.924 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-TE2
max F-Measure· 2015-05-18
0.703
best: 0.921 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-TE2
weighted F-measure· 2015-05-18
0.597
best: 0.885 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-TE1
E-measure· 2015-05-18
0.75
best: 0.927 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-TE1
HCE· 2015-05-18
233
best: 288 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-TE1
MAE· 2015-05-18
0.106
best: 0.031 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-TE1
S-Measure· 2015-05-18
0.716
best: 0.899 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-TE1
max F-Measure· 2015-05-18
0.625
best: 0.89 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-TE1
weighted F-measure· 2015-05-18
0.514
best: 0.846 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-TE3
HCE· 2015-05-18
883
best: 1146 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-TE3
MAE· 2015-05-18
0.098
best: 0.027 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-TE3
max F-Measure· 2015-05-18
0.748
best: 0.936 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konDIS-TE3
weighted F-measure· 2015-05-18
0.644
best: 0.9 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
16konSTARE
AUC· 2015-05-18
0.78
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597

Computer Vision58 results

Object DetectiononDIS-TE4
E-measure· 2015-05-18
0.821
best: 0.944 (MVANet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-TE4
HCE· 2015-05-18
3218
best: 3999 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-TE4
MAE· 2015-05-18
0.102
best: 0.037 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-TE4
max F-Measure· 2015-05-18
0.759
best: 0.912 (MVANet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-TE4
weighted F-measure· 2015-05-18
0.659
best: 0.867 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-VD
E-measure· 2015-05-18
0.785
best: 0.958 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-VD
HCE· 2015-05-18
1337
best: 1660 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-VD
MAE· 2015-05-18
0.113
best: 0.027 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-VD
S-Measure· 2015-05-18
0.745
best: 0.917 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-VD
max F-Measure· 2015-05-18
0.692
best: 0.923 (BEN_Base)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-VD
weighted F-measure· 2015-05-18
0.586
best: 0.896 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-TE2
HCE· 2015-05-18
474
best: 621 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-TE2
MAE· 2015-05-18
0.107
best: 0.028 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-TE2
S-Measure· 2015-05-18
0.755
best: 0.924 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-TE2
max F-Measure· 2015-05-18
0.703
best: 0.921 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-TE2
weighted F-measure· 2015-05-18
0.597
best: 0.885 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-TE1
E-measure· 2015-05-18
0.75
best: 0.927 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-TE1
HCE· 2015-05-18
233
best: 288 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-TE1
MAE· 2015-05-18
0.106
best: 0.031 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-TE1
S-Measure· 2015-05-18
0.716
best: 0.899 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-TE1
max F-Measure· 2015-05-18
0.625
best: 0.89 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-TE1
weighted F-measure· 2015-05-18
0.514
best: 0.846 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-TE3
HCE· 2015-05-18
883
best: 1146 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-TE3
MAE· 2015-05-18
0.098
best: 0.027 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-TE3
max F-Measure· 2015-05-18
0.748
best: 0.936 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononDIS-TE3
weighted F-measure· 2015-05-18
0.644
best: 0.9 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Object DetectiononSTARE
AUC· 2015-05-18
0.78
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-TE4
E-measure· 2015-05-18
0.821
best: 0.944 (MVANet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-TE4
HCE· 2015-05-18
3218
best: 3999 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-TE4
MAE· 2015-05-18
0.102
best: 0.037 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-TE4
max F-Measure· 2015-05-18
0.759
best: 0.912 (MVANet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-TE4
weighted F-measure· 2015-05-18
0.659
best: 0.867 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-VD
E-measure· 2015-05-18
0.785
best: 0.958 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-VD
HCE· 2015-05-18
1337
best: 1660 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-VD
MAE· 2015-05-18
0.113
best: 0.027 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-VD
S-Measure· 2015-05-18
0.745
best: 0.917 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-VD
max F-Measure· 2015-05-18
0.692
best: 0.923 (BEN_Base)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-VD
weighted F-measure· 2015-05-18
0.586
best: 0.896 (BEN_Base+Refiner)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-TE2
HCE· 2015-05-18
474
best: 621 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-TE2
MAE· 2015-05-18
0.107
best: 0.028 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-TE2
S-Measure· 2015-05-18
0.755
best: 0.924 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-TE2
max F-Measure· 2015-05-18
0.703
best: 0.921 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-TE2
weighted F-measure· 2015-05-18
0.597
best: 0.885 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-TE1
E-measure· 2015-05-18
0.75
best: 0.927 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-TE1
HCE· 2015-05-18
233
best: 288 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-TE1
MAE· 2015-05-18
0.106
best: 0.031 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-TE1
S-Measure· 2015-05-18
0.716
best: 0.899 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-TE1
max F-Measure· 2015-05-18
0.625
best: 0.89 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-TE1
weighted F-measure· 2015-05-18
0.514
best: 0.846 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-TE3
HCE· 2015-05-18
883
best: 1146 (BSV1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-TE3
MAE· 2015-05-18
0.098
best: 0.027 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-TE3
max F-Measure· 2015-05-18
0.748
best: 0.936 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononDIS-TE3
weighted F-measure· 2015-05-18
0.644
best: 0.9 (PDFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
RGB Salient Object DetectiononSTARE
AUC· 2015-05-18
0.78
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Scene SegmentationonPST900
mIoU· 2015-05-18
52.8
best: 89.8 (SHIFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Scene SegmentationonMFN Dataset
mIOU· 2015-05-18
45.1
best: 62.7 (RoadFormer+ (ConvNeXt-L))
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Image SegmentationonMARIDA
F1
0.69
Image SegmentationonMARIDA
IoU
0.57
best: 0.67 (ResAttUNet)

Medical20 results

Medical Image SegmentationonKvasir-Instrument
DSC· 2015-05-18
0.9158
best: 0.948 (efficientnetb1)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Medical Image SegmentationonSUN-SEG-Easy (Unseen)
Sensitivity· 2015-05-18
0.42
best: 83.7 (YOLO-SAM 2)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Medical Image SegmentationonSUN-SEG-Hard
Dice· 2015-05-18
0.542
best: 0.876 (LGRNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Medical Image SegmentationonSUN-SEG-Hard
S-Measure· 2015-05-18
0.67
best: 0.685 (UNet++)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Medical Image SegmentationonSUN-SEG-Hard (Unseen)
Sensitivity· 2015-05-18
0.429
best: 0.852 (YOLO-SAM 2)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Medical Image SegmentationonSUN-SEG-Easy
S measure· 2015-05-18
0.669
best: 0.684 (UNet++)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Medical Image SegmentationonSUN-SEG-Easy
mean E-measure· 2015-05-18
0.677
best: 0.687 (UNet++)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Semantic SegmentationonKvasir-Instrument
mIoU· 2015-05-18
0.8578
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Semantic SegmentationonFine-Grained Grass Segmentation Dataset
mIoU· 2015-05-18
48.17
best: 51.96 (D2LS)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Semantic SegmentationonSTARE
AUC· 2015-05-18
0.9158
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Semantic SegmentationonBJRoad
IoU· 2015-05-18
54.88
best: 63.22 (CMNeXt)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Semantic SegmentationonPST900
mIoU· 2015-05-18
52.8
best: 89.8 (SHIFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Semantic SegmentationonMFN Dataset
mIOU· 2015-05-18
45.1
best: 62.7 (RoadFormer+ (ConvNeXt-L))
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Semantic SegmentationonCrackVision12K
mIoU· 2015-05-18
0.60333
best: 0.62982 (Hybrid-Segmentor)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Semantic SegmentationonLaRS
F1· 2023-08-18
15.4
best: 80.2 (TransMari (Mask2Former))
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark arXiv:2308.09618
Semantic SegmentationonLaRS
Q· 2023-08-18
13.9
best: 78.1 (SWIM^2 (Mask2Former))
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark arXiv:2308.09618
Semantic SegmentationonLaRS
mIoU· 2023-08-18
90.1
best: 97.8 (SWIM^2 (Mask2Former))
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark arXiv:2308.09618
Semantic SegmentationonLaRS
μ· 2023-08-18
75.7
best: 79.7 (SWIM^2 (Mask2Former))
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark arXiv:2308.09618
Medical Image SegmentationonSTARE
AUC· 2015-05-18
0.459
best: 0.9924 (DA-Net)
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
Semantic SegmentationonSELMA
mIoU· 2015-05-18
36.2
best: 91.7 (CMX)
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597

Audio16 results

10-shot image generationonKvasir-Instrument
mIoU· 2015-05-18
0.8578
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
10-shot image generationonFine-Grained Grass Segmentation Dataset
mIoU· 2015-05-18
48.17
best: 51.96 (D2LS)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
10-shot image generationonSTARE
AUC· 2015-05-18
0.9158
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
10-shot image generationonBJRoad
IoU· 2015-05-18
54.88
best: 63.22 (CMNeXt)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
10-shot image generationonPST900
mIoU· 2015-05-18
52.8
best: 89.8 (SHIFNet)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
10-shot image generationonMFN Dataset
mIOU· 2015-05-18
45.1
best: 62.7 (RoadFormer+ (ConvNeXt-L))
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
10-shot image generationonCrackVision12K
mIoU· 2015-05-18
0.60333
best: 0.62982 (Hybrid-Segmentor)
SOTA
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Semantic SegmentationonBRIGHT
mIOU· 2025-01-10
64.94
best: 67.63 (ChangeMamba)
BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response arXiv:2501.06019
10-shot image generationonLaRS
F1· 2023-08-18
15.4
best: 80.2 (TransMari (Mask2Former))
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark arXiv:2308.09618
10-shot image generationonLaRS
Q· 2023-08-18
13.9
best: 78.1 (SWIM^2 (Mask2Former))
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark arXiv:2308.09618
10-shot image generationonLaRS
mIoU· 2023-08-18
90.1
best: 97.8 (SWIM^2 (Mask2Former))
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark arXiv:2308.09618
10-shot image generationonLaRS
μ· 2023-08-18
75.7
best: 79.7 (SWIM^2 (Mask2Former))
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark arXiv:2308.09618
10-shot image generationonSELMA
mIoU· 2015-05-18
36.2
best: 91.7 (CMX)
U-Net: Convolutional Networks for Biomedical Image Segmentation arXiv:1505.04597
2D Semantic SegmentationonExtended heartSeg
Average IOU
0.84
2D Semantic SegmentationonMARIDA
F1
0.69
2D Semantic SegmentationonMARIDA
IoU
0.57
best: 0.67 (ResAttUNet)

Miscellaneous2 results

Weather ForecastingonSEVIR
MSE
4.1119
best: 2.9371 (IAM4VP)
Weather ForecastingonSEVIR
mCSI
0.3593
best: 0.4607 (IAM4VP)